Bibliographic record
Abstract
Description Why do institutions get busy but stuck? Courts process thousands of cases while backlogs grow. Permit offices issue approvals while wait times lengthen. Claims processors close files while disputes recur. This paper argues the pattern reflects a binding constraint: human checking capacity is finite. When decisions must be defensible and contestable, there’s a hard limit on how fast work can durably close—not just appear closed, but stay closed. The Obligation Closure Constraint (OCC) formalizes this limit. At any boundary requiring accountable sign-off under credible challenge, durable settlement cannot exceed checking capacity divided by checking effort per case. Exceed it, and the deficit must surface somewhere: backlogs, rework, displacement onto clients, or degraded standards. Three empirical tests validate the framework: · DC FOIA requests: DCR 0.88 led to backlog growth from 1 to 3,360 cases· Philadelphia regulatory appeals: DCR 0.92 led to backlog growth from 202 to 1,983 cases· Vancouver building permits: DCR 1.04 led to the backlog being cleared to zero Same methodology. Different outcomes. Predictions matched reality. Keywordsinstitutional capacity, durable settlement, queue dynamics, verification constraint, backlog analysis, accountability, falsification, OCC Related IdentifiersIsSupplementedBy: https://doi.org/10.5281/zenodo.18073749 (DC FOIA deployment)IsSupplementedBy: https://doi.org/10.5281/zenodo.18076572 (Philadelphia deployment)IsSupplementedBy: https://doi.org/10.5281/zenodo.18077993 (Vancouver deployment)LicenseCC BY 4.0
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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 | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".