Bibliographic record
Abstract
Lack of examples is the bane of philosophical writing. Much too often abstract points are made without practical instances to ground them in experience. At the same time, examples should not be so specific as to draw attention away from the general point being made, and, as alluded to previously, there are times when examples, even if actual events, should not be used to attempt to derive general guiding principles. This is the bane of clinical writing. Too often reliance on case studies results in generalized conclusions and recommendations that are either nearly vacuous or too specific to be broadly applicable. To try to prevent both of these problems while still providing a useful reference point for the discussion that follows, I offer the entirely hypothetical case of Ms. A. Ms. A is sixty-five years old. For the last year or two she has been thinking hard about what life likely holds for her. The main consideration is that both her mother and her father had severe Alzheimer's disease in their early and mid-seventies. Ms. A has given serious thought to ending her life prior to succumbing to the disease. She has taken certain steps: prepared a will, organized her papers, and liquidated her assets. Her children are grown and on their own, and her husband was killed two years earlier in an accident. There are, then, no immediate familial obligations that preclude suicide.
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".