Collapse Index (CI): A Diagnostic Framework for Bounded, Lightweight, and Reproducible Evaluation of System Instability
Bibliographic record
Abstract
This upload contains the preprint “Collapse Index (CI): A Diagnostic Framework for Bounded, Lightweight, and Reproducible Evaluation of System Instability.”The Collapse Index introduces a normalized instability metric designed to reveal hidden brittleness in machine learning systems under benign, non-adversarial perturbations. CI highlights reliability failures that appear stable under standard evaluation methods such as accuracy or confidence-based metrics.The framework emphasizes: • a bounded instability score 0,1 for interpretability • lightweight evaluation requiring only model predictions • dataset-driven perturbation analysis • sealed, reproducible output bundles using cryptographic hashesEach evaluation run produces standardized diagnostics including instability scores, summary tables, and full provenance metadata to support auditability and reproducibility.This deposit includes the full preprint.Associated evaluation artifacts are generated separately and delivered as sealed bundles.Project page: https://collapseindex.orgLicensed under CC BY-NC-ND 4.0.
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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".