Reflections on Advocating for Age-Appropriate Care in B.C.: An Intricate Dance of Crip Time and Governmental Processes
Bibliographic record
Abstract
What happens when you’re chronically ill and your community supports can no longer care for you at home? In B.C., you go into a long-term care facility, if there are beds available, where the average age of clients is 85. Multiple sclerosis (MS) affects four times more women than men, and many women face these decisions in their thirties and forties. Those who enter long- term care facilities often never leave, and are forced to live in a medicalized environment designed for people twice their age. Advocating for change as a disabled activist requires an intricate dance, weaving in the strands of crip time and the political calendar to achieve change for those who are forced to live in a time that is out of step with their needs and desires.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.056 | 0.052 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.029 | 0.056 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".