The Systems Theory of Sustainable Human Capacity (STSHC) and its associated capacity-allocation models operate as a personalized, non-diagnostic workload-shaping and workflow accommodation framework. This model is explicitly non-clinical and non-diagnostic, and it does not function as a medical device or clinical decision tool. All metric indices, composite variables, and suggestion tiers described herein serve exclusively as exploratory, voluntary decision-support tools for individual self-regulation, local calibration, and workplace accommodation. This architecture does not infer underlying capacity with stable precision in a clinically validated manner, nor is it intended for clinical classification or medical use. It must never be utilized by an organization as a performance management classifier or as the sole basis for employment, disciplinary, or medical fitness-for-duty determinations.
Contemporary informational architectures and distributed workspace infrastructures operate predominantly under a mechanistic paradigm that evaluates human output as a linear function of chronological presence. This paper introduces a systems theory of sustainable human capacity that reframes performance as an outcome influenced by multiple biological and environmental capacity layers. By integrating Allostatic Load Theory, Self-Determination Theory, neuroergonomics, and structural choice architectures, the framework describes six primary capacity domains: biological, psychological, cognitive, meaning, relational, and regenerative.
- Introduction: The Upstream Capacity Paradigm
- The Six Pillars of Human Capacity Architecture
- Two-Tier Scoring Logic & Theoretical Grounding
- Contextual Workspace Routing & Accommodations
- Macro-Enterprise Governance: Operationalizing the Cycle
- Organizational Adoption & Implementation Pathway
- Validation Roadmap & Open Science Protocol
- Organizational Impact Model
- Appendix A: Delphi Weight Assignment Process
- Appendix B: Citation & Version Guidance
1.Introduction: The Upstream Capacity Paradigm
Industrial operational standards historically examine human labor through a mechanistic assumption referred to here as the Flatline Fallacy. This structural error assumes that cognitive processing bandwidth is a static, non-fluctuating property that can be extracted uniformly across arbitrary chunks of time, provided the operator exercises sufficient time-management strategies or internal willpower.
Foundational Premise
Capacity is the hidden variable that explains why equal effort does not produce equal outcomes.
Two practitioners operating under identical task demands, identical hours, and identical organizational conditions can produce significantly different outputs — not because of differences in skill, motivation, or character, but because of differences in available capacity across biological, psychological, cognitive, meaning, relational, and regenerative dimensions. The STSHC framework makes this hidden variable visible, measurable as a planning heuristic, and designable as an environmental condition.
The Systems Theory of Sustainable Human Capacity reframes this extractive loop by treating performance and output as downstream outcomes:
When an enterprise system forces uniform output expectations across periods of reduced resources, it can encourage practitioners to override core somatic signatures. Findings from neuroergonomics suggest that sustained interruption and high tracking demands are consistent with a drift toward reduced processing efficiency and increased systemic strain.
2.The Six Pillars of Human Capacity Architecture
The framework separates human system availability into six explicit, non-linear domains, expanding beyond traditional individualistic productivity parameters into an integrated socio-environmental infrastructure model.
The Human Variability Principle
Human capacity is not a fixed trait. It is a dynamic, multi-layered state that fluctuates across time, context, and life circumstance. Variability is not dysfunction. Fluctuation is not failure. The presence of chronic illness, neurodivergent processing profiles, caregiving demands, or grief does not indicate reduced human value — it indicates a different capacity configuration requiring different environmental support. The STSHC framework is built on this principle: human variability is a normal feature of human systems, and organizational design should accommodate it as infrastructure, not manage it as exception.
1Biological Capacity
The foundational physical substrate of the human processing loop. This layer models metabolic homeostatic variables, rest and recovery, sleep architecture configuration, and accommodations required to maintain continuity during chronic illness or dynamic disability states. Anchored in Allostatic Load Theory (McEwen & Stellar). When physiological resource reserves are depleted, the biological subsystem may reach a capacity boundary, and may limit downstream cognitive function regardless of behavioral effort.
2Psychological Capacity
The autonomic stress boundary reflects chronic stress burden. The framework tracks defensive task states, anxiety loops, and internal motivational pressures relative to psychological safety within the immediate work culture. This pillar utilizes the Neurovisceral Integration Model (Thayer et al.) alongside autonomic nervous system literature (Porges) mapping how perceived environmental threat loops can lead to chronic sympathetic activation, reducing attentional resources before a task is initiated.
3Cognitive Capacity
The attentional capacity layer. Maps available prefrontal resources relative to focus fragmentation risks. Grounded in Neuroergonomics and Attention Residue Theory (Leroy). Asynchronous interruptions may require a cognitive context-switch, leaving a residue of attentional activation on the previous task and reducing processing efficiency in the immediate execution queue.
4Meaning Capacity
Human systems are not solely metabolic systems; meaning and semantic alignment may also influence perceived effort. Two individuals expending similar metabolic inputs may experience different exhaustion outcomes based on their internal meaning alignment profile. This observation is not incidental — it points to a structural feature of human performance that metabolic and cognitive models alone cannot account for.
When a milestone aligns with an operator’s values or voluntary sense of service, it can activate intrinsic motivational pathways that lower the activation friction of task initiation. This pillar draws on three converging theoretical traditions:
Self-Determination Theory (Deci & Ryan, 2000) distinguishes between autonomous regulation — acting from genuine interest or personal value — and introjected or extrinsic regulation, which relies on external pressure or internalized obligation. Autonomous regulation may require substantially less executive control and cognitive depletion than its counterparts, suggesting that meaning alignment is not merely motivational but neurologically economical.
Logotherapy (Frankl, 1946/1984) provides the foundational clinical observation that human beings can sustain effort under extreme conditions when meaning is present, and collapse under mild conditions when it is absent. Frankl’s work demonstrates that perceived meaning functions as a capacity multiplier — not by adding resources, but by altering the experienced cost of deploying them. The STSHC framework operationalizes this insight: when the Meaning Alignment Variable (MAV) is high, the perceived activation cost of task initiation is lower, independent of available biological or cognitive resources.
Flow Theory (Csikszentmihalyi, 1990) describes the psychological state in which challenge and skill are optimally matched, producing deep engagement, reduced self-consciousness, and sustained effortful performance with minimal experienced fatigue. Flow states represent the high end of the Meaning Capacity spectrum — conditions in which meaning, skill alignment, and environmental design converge to produce what Csikszentmihalyi termed “optimal experience.” The STSHC framework treats flow not as a lucky accident but as a designable environmental condition.
Meaning in Work Research (Steger et al., 2012) operationalizes work meaning as a measurable construct distinct from job satisfaction or engagement, with independent predictive validity for well-being, performance, and organizational commitment. Steger’s Work and Meaning Inventory (WAMI) demonstrates that meaning in work correlates with positive affect, reduced burnout, and increased prosocial motivation — each of which has downstream implications for Relational and Regenerative Capacity. The MAV in the ACI engine is conceptually anchored to this body of construct validity research.
Note on MAV resolution: The MAV is a coarse heuristic approximation of a multi-dimensional construct. The theories cited above — particularly SDT and the WAMI — each employ validated multi-item instruments that offer substantially higher construct resolution than a single self-report variable. Organizations seeking higher-resolution meaning measurement may administer the WAMI alongside the ACI as part of Phase 3 validation participation. Future versions of the ACI may incorporate a multi-item MAV subscale pending empirical validation.
5Relational Capacity
The co-regulatory infrastructure layer. STSHC integrates relational support not as an emotional luxury, but as a load-sharing function. Human beings natively co-regulate, co-resource, and co-recover. Grounded in Social Baseline Theory (Coan), which suggests that the brain may treat social interdependence as a baseline condition; isolation may require more effort to maintain homeostasis.
6Regenerative Capacity
Modeled as the layer that supports renewal. Regenerative capacity tracks the presence of curiosity loops, playful experimentation, deep flow states, physical somatic movement, and creative novelty. Grounded in the Broaden-and-Build Theory (Fredrickson) and neurobiological dopamine novelty loops (Bunzeck & Duezel, 2006). Actively scheduling states of play and intrinsic curiosity expands an individual's available behavioral repertoire, supporting durable cognitive and psychological resources over time.
3.Two-Tier Scoring Logic & Theoretical Grounding
Validation Status
The SC and ACI metrics are proposed composite indices. Individual component variables (MEI, ASI, PRS, ABC, SFV, MAV, RCI, RCV) draw from established constructs in peer-reviewed literature (see citations throughout). The specific weighting and aggregation method presented here is a theoretical framework awaiting empirical validation. A validation study protocol is outlined in Section 7.
3.1 The Core System Coefficient (SC) Heuristic
Designed for individual creators, freelancers, and standalone public web interfaces. Maps five primary metrics on a 1 to 5 scale:
MEI — Metabolic Energy Heuristic · ASI — Autonomic Safety Heuristic · PRS — Self-reported focus · ABC — Affective Burden Coefficient · SFV — Sensory Friction Variable
MEI, ASI, and PRS are positive inputs. ABC and SFV are negative inputs — active drains that siphon prefrontal resources before execution begins.
Self-Report Variance Caveat
SC scores are planning heuristics, not objective measurements. Because all five variables are self-reported, scores carry inherent variance across individuals, mood states, and reporting contexts. Some variables — such as MEI (physical energy) — may be more reliably anchored to somatic signals than others, such as ABC (affective burden), which is highly state-dependent and subject to interoceptive variation. Practitioners should treat SC scores as directional planning inputs rather than precise capacity readings. Day-to-day fluctuations of 2–3 points are expected and do not necessarily indicate meaningful capacity change. The instrument’s value lies in repeated-use pattern recognition over time, not in single-session precision.
On the Normalization Constant (+7): The raw equation produces a theoretical range of −7 to +13. A fixed normalization constant of +7 is applied to every score, shifting the operational range to a clean 0 to 20 scale without altering the relative weight of any variable. The constant is a transparent arithmetic adjustment, not a clinical adjustment.
3.2 The Advanced Capacity Index (ACI) Engine
Designed for enterprise software integrations and workforce planning applications:
MAV — Meaning Alignment Variable · RCI — Relational Capacity Index · RCV — Regenerative Capacity Variable. Weights are determined via a local Delphi method and normalized to a total sum of 1.0 (see Appendix A for the full process). Because each variable is scored on a 1–5 scale and weights sum to 1.0, the raw weighted sum ranges from 0 to 5. Multiplying by 4 maps this to the same 0–20 scale as the SC, allowing both metrics to share the same routing table. Organizations that require an immediate starting point may use one of the three pre-set profiles below, selecting the closest match to their team context and adjusting from there.
Default Weight Profiles
| Profile | MEI | ASI | PRS | MAV | RCI | RCV | Total |
|---|---|---|---|---|---|---|---|
| Knowledge Work | 0.15 | 0.20 | 0.20 | 0.20 | 0.15 | 0.10 | 1.00 |
| Caregiving / Service | 0.20 | 0.20 | 0.15 | 0.15 | 0.20 | 0.10 | 1.00 |
| Creative / Research | 0.15 | 0.15 | 0.15 | 0.25 | 0.10 | 0.20 | 1.00 |
These profiles are illustrative starting points, not empirically validated norms. Organizations are encouraged to treat them as an initial calibration to be refined through the Delphi process described in Appendix A.
Note on scaling: Multiply each weight by its corresponding variable score (1–5 scale), sum the six products, then multiply by 4 to align with the SC 0–20 scale. Example using Knowledge Work profile at mid-range scores (all variables = 3): 4 × [(0.15×3=0.45) + (0.20×3=0.60) + (0.20×3=0.60) + (0.20×3=0.60) + (0.15×3=0.45) + (0.10×3=0.30)] = 4 × 3.00 = 12.0 (Conservation State).
4.Contextual Workspace Routing & Accommodations
| SC Score | Tier | Suggested Accommodations |
|---|---|---|
| 17–20 | Full Availability | Conditions may suit higher-demand workflows; protected focus period; reduced real-time communication exposure. |
| 12–16 | Baseline Availability | Proceed with standard project tasks at standard pacing. |
| 7–11 | Conservation State | Support continuity with lower-friction administrative tasks. |
| 0–6 | Restoration State | Suggested reduction in higher-demand tasks; protect time for restoration. |
Cut-Point Notice
Tier boundary values (0–6, 7–11, 12–16, 17–20) are theoretically derived and have not yet been empirically calibrated. Cut-points will be refined during Phase 3 validation using known-groups comparisons and convergent validity data. Organizations should treat tier boundaries as approximate planning ranges, not precise thresholds. A score of 16 and a score of 17 should not be interpreted as meaningfully different capacity states.
4.1 Workload-Reduction Guardrails
Regardless of the total mathematical sum, if an operator's self-reported physical foundation or environmental safety score registers at a critical baseline of 1, the framework suggests scaling down active milestone expectations, routing communications into asynchronous queues, and reducing real-time communication exposure.
4.2 Neurological Variance & Support Profile Design
The framework treats neurological variance and neurodivergent support profiles not as medical pathologies, but as distinct profiles requiring different accommodations.
- Monotropic Attention Parameters: Deep, sustained focus in some individuals. Suggested: protected focus periods to reduce real-time communication exposure.
- Variable Attention Parameters: Interest-based attention loops. Operators may rotate tracks every 20 minutes to reduce activation friction.
- Dynamic Physical Baselines: Manage autoimmune or chronic fatigue states via dynamic scope planning, shifting deliverables during physical health flares. These profiles are illustrative, not exhaustive. Accommodations should be co-designed with each operator.
5.Macro-Enterprise Governance: Operationalizing the Cycle
The STSHC framework uses cyclic pacing infrastructure with alternating wave cycles for group delivery:
During active production phases, communication architectures utilize batch communication windows at set times each day (e.g., 10:00 AM and 3:30 PM) to reduce interruptions.
The 1-week maintenance phase includes a pause on new feature rollouts, redirecting processing bandwidth to clear documentation gaps, refactor legacy repositories, and eliminate technical debt. On day five, management conducts the voluntary Team Planning Review using the aggregate SC dataset before initializing the next output wave.
6.Organizational Adoption & Implementation Pathway
Transitioning an organization from traditional chronological tracking to a sustainable capacity framework requires a structured, multi-phase implementation roadmap. Ownership sits within HR / People Operations, serving as the internal capacity steward.
Capacity Discovery & Baseline Audit
HR executes an audit of existing digital communication patterns, assessing focus fragmentation metrics, meeting density, and context-switching overhead. Three baseline measurements are established as 90-day comparison anchors: voluntary attrition rate, team satisfaction scores via anonymous pulse survey, and estimated daily context-switching load per role category.
Heuristic Widget Integration
HR deploys the MYCAPACITI Workspace Planning Guide into the daily team layout. Participation is explicitly optional. HR communicates clearly that SC data is never used for performance evaluation, disciplinary action, or employment decisions. A brief orientation session covers the scoring logic, normalization constant, and routing tiers.
Strategy Wave Launch
The organization formally adopts the alternating 6+1 wave cycle structure. The frontend layout switches to a visual timeline dashboard highlighting active production versus upcoming maintenance windows. Batch communication windows are activated at set times daily.
90-Day Evaluation Review
HR conducts a structured evaluation against three success indicators:
| Indicator | Method | Target |
|---|---|---|
| Tool Adoption Rate | Daily active usage logs | ≥60% of eligible team |
| Burnout & Attrition Signal | vs. Phase 1 baseline | Directional improvement |
| Team Satisfaction Score | Anonymous pulse survey | Net positive shift |
Findings are summarized in a Capacity Health Report, informing whether the organization proceeds to full enterprise ACI integration or adjusts parameters for the next operational period.
Ethical Firewall Protocol – Individual Data Visibility
Individual SC/ACI scores are encrypted client-side and are never stored on any server in identifiable form. The operator's own device retains the only copy of their individual time-series data. Organizational dashboards receive only aggregated, team-level statistics (mean, median, distribution bin counts) with a minimum cell size of five operators. Any aggregate with n < 5 is suppressed.
No manager, HR business partner, or system administrator can retrieve an individual operator's score – by design, not by policy alone. The framework is voluntary at point of entry and irreversible at point of aggregation. Operators may delete their own data at any time without notification to management.
The 90-day evaluation process uses only team-level aggregates, except for anonymized aggregate data used for instrument validation studies (with separate explicit consent). Organizations that cannot deploy this architecture should not claim STSHC compliance.
Anti-Surveillance Commitment
The STSHC framework is explicitly not a surveillance system, a productivity monitoring tool, or a performance benchmarking architecture. SC and ACI scores are designed to be invisible to management by default. The framework does not produce rankings, comparisons between practitioners, or aggregate efficiency scores. It produces only voluntary, self-reported planning inputs that belong to the practitioner who generates them.
Any deployment that uses SC or ACI data to monitor, rank, compare, or evaluate individual practitioners violates the foundational design principles of this framework and should not claim STSHC compliance. Capacity is a personal planning variable. It is not an organizational performance metric.
Note on compliance status: STSHC compliance is currently self-declared. Organizations adopting this framework are encouraged to document their deployment decisions as an internal governance record. A formal third-party certification process is planned for Version 2.0.
7.Validation Roadmap & Open Science Protocol
The framework described above is released as a theoretical standard for pre-validation use. Empirical validation is ongoing. Below is the pre-registered validation protocol.
Expert Review (n = 5–10)
Experts rate each SC/ACI item for relevance (1–5) and clarity (1–5). Item-Content Validity Index (I-CVI) target > 0.78.
Reliability Study (n = 30)
Participants complete SC/ACI twice, 7–14 days apart. Intraclass Correlation Coefficient (ICC) target > 0.70.
Validation Study (n = 50–100)
Participants complete SC/ACI alongside NASA-TLX (workload), WHO-5 (well-being), and single-item sleep/stress measures. Hypothesised moderate correlations. Known-groups comparison: higher burnout, chronic illness, and caregiving burden are predicted to show lower SC/ACI scores.
Open Science Commitment
Anonymised aggregate data and analysis scripts will be published alongside any validation paper. Individual scores are never shared.
Selected Supporting Literature
- Whole-day workload measured via EMA was associated with decreased next-day processing speed (standardized estimate = −0.10, 95% CI −0.18 to −0.01) in a within-person study of workers. (Hernandez et al., 2024, Current Psychology)
- Heart rate fragmentation (HRF) demonstrates efficacy as a potential allostatic load biomarker, distinguishing psychosocial stress reactivity conditions in healthy adults. (Chan & Andersen, Applied Psychophysiology and Biofeedback, 2026. DOI: 10.1007/s10484-025-09721-1)
- Neurovisceral Integration Model shows HRV correlates with glucose, cortisol, and inflammatory markers – physiological grounding for the Psychological Capacity pillar. (Thayer et al., 2006)
- NASA-TLX has demonstrated lack of convergent validity and sensitivity in HCI tasks, supporting the need for alternative whole-day frameworks. (Babaei et al., International Journal of Human-Computer Studies, 2025. DOI: 10.1016/j.ijhcs.2025.103515)
Reference List
- Hernandez, R., Jin, H., Pyatak, E. A., Roll, S. C., & Schneider, S. (2024). Workers’ whole day workload and next day cognitive performance. Current Psychology, 43(3), 2005–2018. https://doi.org/10.1007/s12144-023-04400-y
- Chan, J. F., & Andersen, J. P. (2026). Heart rate fragmentation: A novel analytic approach to detect allostatic load among healthy adults. Applied Psychophysiology and Biofeedback, 51, 29–47. https://doi.org/10.1007/s10484-025-09721-1
- Thayer, J.F., et al. (2006). The neurovisceral integration model of emotion regulation. Neuroscience & Biobehavioral Reviews.
- Leroy, S. (2009). Why is it so hard to do my work? The challenge of attention residue when switching between work tasks. Organizational Behavior and Human Decision Processes, 109(2), 168–181.
- Deci & Ryan (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
- Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. American Psychologist, 56(3), 218–226.
- Coan, J. A. & Sbarra, D. A. (2015). Social baseline theory: The social regulation of risk and effort. Current Opinion in Psychology, 1, 87–91.
- McEwen, B. S. & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093–2101.
- Bunzeck & Düzel (2006). Absolute coding of stimulus novelty in the human substantia nigra/VTA. Neuron, 51(3), 369–379.
- Frankl, V. E. (1984). Man’s search for meaning (3rd ed.). Washington Square Press. (Original work published 1946)
- Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
- Steger, M. F., Dik, B. J., & Duffy, R. D. (2012). Measuring meaningful work: The Work and Meaning Inventory (WAMI). Journal of Career Assessment, 20(3), 322–337. https://doi.org/10.1177/1069072711436160
- Babaei, E., Dingler, T., Tag, B., & Velloso, E. (2025). Should we use the NASA-TLX in HCI? A review of theoretical and methodological issues around mental workload measurement. International Journal of Human-Computer Studies, 201. https://doi.org/10.1016/j.ijhcs.2025.103515
8.Organizational Impact Model
The STSHC framework is designed as a capacity infrastructure, not a performance optimization tool. The distinction matters: performance optimization frameworks extract output from existing capacity; capacity infrastructure frameworks expand the sustainable availability of that capacity over time. The expected downstream effects of implementing STSHC-aligned environments include the following theoretical implications, drawn from the supporting literature cited throughout this document.
Important limitation: All organizational impact projections described in this section are theoretical implications drawn from supporting literature. No controlled trial of the STSHC framework has been conducted to date. Outcomes will vary by organizational context, leadership culture, implementation fidelity, and team composition. These projections should be treated as directional hypotheses to be tested within each organization’s 90-day evaluation cycle, not as guaranteed outcomes.
8.1 Retention and Attrition
Research on allostatic load (McEwen & Stellar, 1993) consistently suggests that chronic environmental mismatch — forcing uniform output expectations across variable capacity states — is associated with elevated stress burden and increased voluntary attrition. Organizations implementing capacity-aware scheduling, batch communication windows, and the 6+1 wave cycle may observe reduced attrition signals over 12–24 month horizons. No specific attrition reduction figure is implied; potential organizational benefits will vary by baseline culture and implementation fidelity.
8.2 Cognitive Output Quality
Attention Residue Theory (Leroy, 2009) predicts that reducing context-switching frequency is associated with higher-quality cognitive output per unit of time worked. Organizations that implement protected focus periods and batch communication windows may observe improvements in output quality, error reduction, and first-pass completion rates on complex cognitive tasks. These are expected directional effects, not guaranteed performance gains.
8.3 Interruption Cost Reduction
Each unplanned interruption imposes an attentional recovery period before full re-engagement can occur. For a team of 20 practitioners experiencing an average of 10 unplanned interruptions per day, the theoretical attention recovery cost exceeds 30 hours of aggregate productive time per week — prior to any task completion. Organizations implementing batch communication windows at two defined daily intervals may substantially reduce this interruption overhead across their practitioner population.
8.4 Onboarding and Integration Speed
Relational Capacity (Pillar 5) models the load-distribution function of high-trust networks. Social Baseline Theory (Coan, 2015) suggests that practitioners entering low-trust or low-support environments may expend significantly more baseline energy maintaining homeostasis, leaving less available for skill acquisition. Organizations with strong co-regulatory infrastructure — transparent mentorship structures, psychological safety, collaborative load-sharing — may observe faster onboarding integration and lower 90-day attrition.
8.5 Accessibility and Inclusion Infrastructure
The STSHC framework operationalizes accommodation as a design variable rather than a compliance requirement. Organizations implementing capacity-aware structures — dynamic scope planning, protected focus windows, asynchronous communication defaults — may observe increased psychological safety, broader talent pool access, and reduced accommodation friction for practitioners with neurodivergent profiles, chronic illness, or fluctuating physical capacity. These are inclusion gains with associated retention, recruitment, and innovation implications.
8.6 Innovation and Creative Output
Regenerative Capacity (Pillar 6) is grounded in the Broaden-and-Build Theory (Fredrickson, 2001) and neurobiological dopamine novelty loops (Bunzeck & Düzel, 2006). Organizations that build curiosity loops, protected exploration time, and playful experimentation into their capacity cycles may observe expansion of the available behavioral repertoire at the team level — the organizational equivalent of the individual thought-action expansion described in the supporting literature. Creative output and novel problem-solving are expected downstream effects of sustained Regenerative Capacity investment.
8.7 Summary of Expected Downstream Effects
| Organizational Dimension | STSHC Mechanism | Expected Direction |
|---|---|---|
| Voluntary attrition | Allostatic load reduction via capacity-aware scheduling | Directional decrease |
| Cognitive output quality | Attention residue reduction via batch communication | Directional improvement |
| Interruption overhead | Protected focus periods; batch windows | Directional decrease |
| Onboarding integration speed | Relational co-regulatory infrastructure | Directional improvement |
| Accessibility & inclusion | Accommodation as design variable | Directional improvement |
| Creative & innovation output | Regenerative Capacity scheduling | Directional improvement |
A.Appendix A: Delphi Weight Assignment Process
The following six-step process allows an organization to derive locally calibrated ACI weights without external consultation. The process draws on the Delphi method, a structured expert consensus technique, adapted here for internal HR and team-leadership panels.
Estimated Time Commitment
Total: approximately 1.5–2 hours across two rounds, typically conducted one week apart. Rounds 1 and 2 can be completed asynchronously via a shared form.
Assemble a Panel
Recruit 5–10 internal stakeholders with diverse roles — for example, HR leads, team managers, a union representative if applicable, and one or two individual contributors. Role diversity reduces anchoring bias in the weighting process.
Round 1 — Anonymous Independent Rating
Each panelist independently assigns a weight between 0 and 1 to each of the six ACI pillars (MEI, ASI, PRS, MAV, RCI, RCV) based on their perception of relevance to the team's operational context. Panelists do not discuss or share ratings at this stage. Raw weights do not need to sum to 1.0 at this point — normalization happens in Step 5.
Aggregate and Share Results
Calculate the mean and standard deviation for each pillar across all panelist responses. Share the aggregate distribution back to the panel anonymously — panelists see group results but not individual attributions. Flag any pillar where the standard deviation exceeds 0.15, indicating meaningful disagreement that warrants discussion in Round 2.
Round 2 — Informed Re-Rating
Panelists revise their weights in light of the group distribution. They may submit a brief written rationale (1–2 sentences) if their revised rating remains more than one standard deviation outside the group mean. This surfaced reasoning is shared anonymously with the full panel before final aggregation.
Normalize to 1.0
Average the Round 2 weights across all panelists for each pillar. Divide each pillar average by the sum of all six averages so that the final weights total exactly 1.0. These normalized values become w1 through w6 in the ACI equation. Document the final weights and the panel composition for transparency.
Annual Review
Revisit the weight profile annually, or whenever team composition, role structure, or organizational priorities change significantly. A full re-run of Steps 1–5 is recommended after any major organizational restructure and typically requires 1.5–2 hours across two asynchronous rounds. Minor recalibrations may be handled via a single-round panel check-in of approximately 20 minutes.
Example normalization: If Round 2 averages are MEI=0.20, ASI=0.25, PRS=0.18, MAV=0.22, RCI=0.17, RCV=0.14 — the sum is 1.16. Dividing each by 1.16 gives: MEI=0.172, ASI=0.216, PRS=0.155, MAV=0.190, RCI=0.147, RCV=0.121. These sum to 1.001 (rounding); adjust the largest weight by the rounding remainder to reach exactly 1.000.
Organizations that complete this process and document their panel composition, round results, and final weights may describe their ACI implementation as locally calibrated. Those using pre-set profiles from Section 3.2 should note this in any internal reporting.
B.Appendix B: Citation & Version Guidance
How to Cite This Document
Timothy, A. (2025). A systems theory of sustainable human capacity: Version 1.0 core standard (STSHC_PREPRINT_WHITE_PAPER_v1.0_CORE_STANDARD). Pre-validation release. https://mycapaciti.com
APA Format
Timothy, A. (2025). A systems theory of sustainable human capacity (v1.0). STSHC Preprint. Pre-validation release.
Version Control
This document is Version 1.0. It represents the initial theoretical specification of the STSHC framework, released for institutional review, academic consultation, and early organizational adoption prior to empirical validation.
- Version 1.0 (current): Core theoretical standard. Six-pillar architecture, SC/ACI scoring model, implementation pathway, ethical firewall, validation roadmap.
- Version 1.1 (planned): To be released following Phase 1 content validation (I-CVI results). Will incorporate expert panel feedback on item relevance and clarity.
- Version 2.0 (planned): To be released following completion of Phase 2 and Phase 3 validation studies. Will incorporate empirical reliability and validity data.
Change Log
| Version | Date | Key Changes |
|---|---|---|
| 1.0 | 2025 | Initial release. Six pillars, SC/ACI model, ethical firewall, validation roadmap, organizational impact model, Delphi weight process. |
Versioning Principles
Minor versions (1.x) incorporate expert feedback, citation corrections, and language refinements that do not alter the core theoretical architecture. Major versions (x.0) are triggered by empirical validation milestones, significant structural additions, or changes to the scoring model. All versions will be archived and remain citable in their original form. Organizations referencing this framework in internal documentation should record the version number used at the time of adoption.
Permissions and Reuse
The STSHC framework is released as an open theoretical standard. Academic citation, institutional review, and non-commercial adoption are permitted with attribution. Organizations wishing to deploy STSHC-aligned tools commercially or certify STSHC compliance programs should contact the framework author to ensure deployment integrity and ethical firewall adherence.