Obligation Closure Constraint (OCC): Formal Specification and Falsification Protocol
Bibliographic record
Abstract
This document presents the Obligation Closure Constraint (OCC) as a formal specification and test protocol for consequence-bearing systems where outcomes depend on accountable human decisions under credible challenge. It defines a minimal measurement grammar that separates work that merely appears "closed" from durable settlement (work that stays closed over a declared horizon), and it treats displacement—work pushed into downstream queues, shadow channels, or onto users—as an explicit accounting state rather than hidden residual. The specification formalizes a finite verification-and-closure channel: a limited capacity to reduce uncertainty to a declared fidelity standard, make defensible determinations, and settle obligations despite drift from changing rules, interfaces, or adversaries. When demand persistently exceeds this capacity, the excess cannot vanish; it must surface as measurable signatures such as reopenings/return-work, tail thickening and delay growth, displacement, and degraded auditability/actuation, with possible hysteresis after saturation. The document provides an empirical charter: what must be instrumented before claims are permitted, how to avoid circular or contaminated metrics, how to condition results on contestability and reopen channels, what causal claims are allowed at each evidence tier, and what observations would count against the OCC. The aim is audit-ready diagnosis and adversarial testing that prevents boundary drift, proxy substitution, or post-hoc redefinition of success. Empirical Validation: Three Tier-1 deployments have been executed using this protocol, 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, unresolved 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, unresolved 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, unresolved stock declined from 494 to zero. DOI: 10.5281/zenodo.18077993 These deployments confirm the protocol's core discriminative capacity: identical methodology applied to different administrative boundaries produces divergent regime classifications that match observed stock dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".