Bibliographic record
Abstract
I was recently asked to inspire members of an audience who support individuals with developmental disabilities and mental health concerns. Jean Vanier (1998), the Canadian founder of the worldwide L’Arche movement, in his most recent book, Becoming Human, wrote of his belief that we are blessed to walk alongside the lives of individuals with developmental disabilities. It was a true privilege to tell my audience about some of the people I have met in my work as a psychiatrist who have graced my life and inspired me. I told the audience that several weeks prior to my presentation, I had met a man while holding a psychiatric clinic in a town far from my hometown and my children. He was from a smaller town farther still from where either he or I were born. With scientific rigor, I had gathered in advance years of his documented testing and re-testing. This documentation told a story of this man’s life, colored by chronic, intermittent aggression, noncompliance, a moderate degree of developmental disability, and a seizure disorder. As he walked into the assessment room, I felt at ease quickly as I watched his smile and felt the warmth and strength of his handshake. I was quickly inspired as he granted me the privilege of beginning to trust me, to the extent that in the first 15 minutes of our discussion, he explained:
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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.035 |
| Insufficient payload (model declined to judge) | 0.015 | 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".