Bibliographic record
Abstract
I do not have a clear recollection of the circumstances of my first meeting with Jack Morgan, but I do remember where we met. I suspect that more out of the goodness of his heart than to any great scholarship on my part in the field of death and bereavement, he had invited me to present a paper at one of his many much-attended legendary annual conferences, which he organized at King’s College in London, Ontario. Aware of my rapidly failing health, he very kindly arranged to meet in Toronto and drive Ann, my wife, and me to London. From the manner in which he walked, to me he seemed like a huge, friendly, and kindly teddy bear. That was the first impression I formed of him; it has stayed with me and will continue to stay with me. (I half expected him to scratch his chest as we sat down in the hotel where we had arranged to meet. It would not have surprised me had he ordered a pot of honey for himself.) His demeanor was gentle, his voice soft, his smile infectious, his gestures controlled; one could not help but like him instantly. In no time at all we became good friends.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.079 | 0.054 |
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".