Bibliographic record
Abstract
We are grateful to J. Paul-Emile Roy, Beth Tennant and Ari Sahakoglu for assistance in gathering financial information, Steve Fortin, who provided assistance in translating several French disclosures in financial statements to English, Erin Harvey at the University of Waterloo Statistics Consulting Laboratory for statistical assistance, and to Sati Bandyopadhyay, Len Eckel, Duane Kennedy and Gord Richardson for helpful comments on earlier versions of this paper. ii This study addresses going concern disclosures of Canadian companies during the 1987-2002 time period. For these companies, the textual disclosures (e.g., presence or absence of a going concern footnote warning potential users that the company is in danger of failure) in the last available financial reports prior to failure were examined to determine if there was a warning of the going concern assumption being in jeopardy. In addition, three Canadian and two US bankruptcy prediction models were applied to these samples- Springate (1978), Altman and Levallee (1980), and Legault and
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.430 | 0.228 |
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; the direct Gemma label and the distilled Codex classifier 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".