70 years of the <i>Journal of Clinical Pathology</i> : <i>Quo vadis</i> ?
Bibliographic record
Abstract
It is indeed an honour for me to take over the role of Editor-in-Chief (EIC) for the Journal of Clinical Pathology ( JCP ) in 2017. Over the past 10 years or so, I have worked with the last two EICs, Runjan Chetty (Toronto) and then latterly with Cheok Soon Lee (Sydney) and am grateful for their contributions in leading the journal to the current level and they have left big shoes to fill. In an era with the continuing proliferation of journals on a daily basis, much like neoplasms in pathology, there are immense challenges faced by both established and neo-journals alike in maintaining the captive audience and catering for needs of the readers. Another hurdle the journal is confronted with is the spread of superspecialisation and the avalanche …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".