Bibliographic record
Abstract
My strong desire to enter the helping profession has been fuelled by my experiences as a consumer-survivor of the mental health system who is recovered and is symptom-free. And yet, driven by concomitant hopes of changing the system under which I found my care and the care of those around me to be flawed at best, and in order to relate to both consumers and practitioners the need for a recovery paradigm and for empowerment and hope, I found that in the academic world, I "passed." For fear of discrimination, tokenization, and the discreditation of my ideas, I took on the role of practitioner-in-training, never disclosing my identity as a consumer-survivor, no matter how much I felt this unique perspective could contribute to a discussion. I was torn. Had I not entered this field in order to bring about change and instill hope based on my story? Feeling hypocritical, I nonetheless decided that it would be best to delay self-disclosure until I achieved professional status.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".