Bibliographic record
Abstract
RiSUMtCette analyse, bas~e sur l'Enqute nationale de 2004 sur les probkmes de la justice civile au Canada, r~vle que la mauvaise sant6 et l'invalidit6 sont likes A une incidence plus 6lev~e de treize categories de problkmes de justice civile parmi quinze qui ont &6 identifies.Les personnes qui souffrent d'un problme de sant6 ou d'invalidit6 sont plus susceptibles que le reste de la population percevoir que les probl~mes sont r~gl~s de fa~on inequitable, de trouver que la situation s'est aggrav~e dans les cas odi les probl~mes n'ont pas t6 r~gl~s, et d' prouver des probl~mes persistants faisant r~f~rence A des probl~mes non r~solus qui durent depuis au moins trois ans.Les travaux laissent supposer que les personnes ayant des probl~mes de sant6 ou d'invalidit6 6prouvent un sentiment d'exclusion sociale un degr6 relativement 6lev6.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".