Ghost Busting, Taking the Sheet Off the Ghost
Bibliographic record
Abstract
> Copublished with permission in Rheumatology, The Journal of Rheumatology, Clinical and Experimental Rheumatology, Clinical Rheumatology, Rheumatology International, Modern Rheumatology, Zeitschrift für Rheumatologie and Reumatología clínica. All rights reserved in respect of Rheumatology, ©The Authors 2016. For The Journal of Rheumatology, ©The Journal of Rheumatology 2016. For Clinical Rheumatology, ©Clinical Rheumatology 2016. For Clinical and Experimental Rheumatology, ©Clinical and Experimental Rheumatology 2016. For Rheumatology International and Zeitschrift für Rheumatologie, © Springer-Verlag GmbH Berlin Heidelberg 2016. For Modern Rheumatology, ©Japan College of Rheumatology 2016. For Reumatología clínica,© Elsevier España, S.L.U. Barcelona, 2016. Ghost authorship, defined as when an individual has made a substantial contribution to writing, research, or editing of a manuscript, but is neither listed as an author nor appropriately acknowledged in the paper, is a cause for concern in biomedical publishing. A reader needs to be confident that the paper they are reading is the work of those prepared to take responsibility for it. The question of ghost authorship examines the criteria of what qualifies a person to be an author of a paper. Where does contribution end and authorship begin? … Address correspondence to K. Wilson; E-mail: editorial{at}rheumatology.org.uk
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.169 | 0.123 |
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".