Bibliographic record
Abstract
DESCRIPTION OCC is a falsifiable constraint on completion rates in systems where humans must make defensible, contestable decisions. The core claim: Durable completion rate cannot exceed verification capacity divided by verification effort per item. When more decisions require checking than capacity allows, the gap must manifest as observable outputs—backlog growth, rework, displacement to other parties, declining quality standards, or persistent degradation after overload. Modern large-scale coordination increases complexity, change rate, and required standards faster than human decision-making capacity scales. This creates a structural mismatch that worsens over time independent of institutional intent or effort. The constraint applies to any consequence-bearing boundary requiring accountable human judgment: courts, healthcare administration, permitting, insurance adjudication, safety certification, and high-stakes review processes in software and operations. This document provides the theoretical foundation for OCC. Companion records provide formal specification, measurement protocol, and empirical deployments. Empirical Deployments (Tier-1): Three case studies have been executed using the OCC framework, demonstrating its ability to discriminate between sustainable and overloaded regimes: Washington, DC FOIA Request Processing (2020–2025) — Regime diagnosis: Busy but stuck. DCR ≈ 0.88, stock grew from ~1 to >3,360 cases. DOI: 10.5281/zenodo.18073749 Philadelphia L&I Appeals Processing (2010–2018) — Regime diagnosis: Busy but stuck. DCR ≈ 0.92, stock grew from 202 to 1,983 cases. DOI: 10.5281/zenodo.18076572 City of Vancouver Building Permits (2018–2025) — Regime diagnosis: Sustainable. DCR ≈ 1.04, stock declined from 494 to zero. DOI: 10.5281/zenodo.18077993 These deployments confirm the framework's core discriminative capacity: identical methodology applied to different systems produces divergent regime classifications that match observed stock dynamics. Related Identifiers Add all three as "IsSupplementedBy": https://doi.org/10.5281/zenodo.18073749 https://doi.org/10.5281/zenodo.18076572 https://doi.org/10.5281/zenodo.18077993
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".