Bibliographic record
Abstract
Nell Toussaint is not well. In recent years, she has been diagnosed with uterine fibroids, uncontrolled hypertension, nephrotic syndrome, poorly controlled diabetes, hyperlipidemia, and a pulmonary embolism. She also suffers from decreased mobility, shortness of breath, and-perhaps not surprisingly, given her other ailments-anxiety. Toussaint is an indigent undocumented immigrant living in Canada who has been trying to secure medical coverage in the federal courts. In the process, she has sacrificed the medical confidentiality that most of us ordinarily enjoy. Toussaint first came to Canada from Grenada as a visitor in 1999 and remained after the term of her visa expired. At first, she earned enough to sustain a living, but in 2006, her health began to deteriorate, and she was no longer able to work. Although she has received some medical care since then, it has been sporadic, on an emergency basis, and at great expense. When Toussaint applied for medical health coverage under Canada's Interim Medical Health Program, which covers the cost of emergency medical care for legally admitted indigents, her application was rejected. She challenged the decision in federal court on the grounds that her right to life and security of the person under the Canadian Charter had been violated and that the denial of coverage was discriminatory.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".