Bibliographic record
Abstract
I have taken a wandering course from juvenilities through personalities perhaps to senilities, attempting neither to write a history of my time nor to make a treatise on journalism; but hoping that here and there some kind friends unknown may find something as interesting for them to read as it has been for me to remember. Edward P Mitchell Out of personal preference, I have desisted from annual or even more frequent missives from the Editor, preferring to let the content of the Journal of Clinical Pathology reflect the direction of the editorial team. However, I feel compelled to write something now, partly out of self-indulgence but mainly to document my gratitude to several who have helped me to achieve a lifelong goal. If the truth be told, ever since I first picked up and read a pathology journal as a trainee, I have wanted to be an editor. This desire became even greater as I started submitting papers to these very journals that formed part of my monthly reading diet. I pondered what it would be like to be at the other end of the publishing highway. What actually happens to a paper, what comments are generated and how the final decision is formulated formed a nidus of curiosity that lingered, smouldered and often times erupted (especially when a paper was rejected!). I needed to see what the other side of publishing was all about. When the opportunity to apply for the Editors position of the Journal of Clinical Pathology arose, I was filled with a tremendous sense of anticipation and excitement that would have done a ...
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".