Bibliographic record
Abstract
I live with a chronic, incurable disease called rheumatoid arthritis (RA). According to my healthcare system, this makes me a patient. But I can tell you I consider myself a lot of other things before I introduce myself to anyone as a patient or as a person who lives with RA. In fact those words (patient, person living with RA) are the ones I choose only when I’m in the healthcare system or when I’m part of some type of project that includes a patient perspective. Those words make me feel less empowered, less influential and less skilled than the others at the table in those situations. I am quite certain that when I’m in professional situations where I offer myself up as a patient, I am looked at ‘differently’—and not in a ‘good way’ of differently. I contend that most patients don’t identify themselves as a patient first and foremost–this is a label imposed on us by our healthcare systems. Sarah Riggare is a person who lives with Parkinson’s disease and who uses a stunning visual and explanation to convey how little time she spends with her neurologist annually: ‘I visit my neurologist twice a year, for about 30 min. That is one hour per year. The rest of the year’s 8765 hours, I spend in self-care’.1 This is similar to the time I spend annually with my …
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".