WORK: A historical evaluation of the impact and evolution of its editorial board
Bibliographic record
Abstract
OBJECTIVES: A historical review of the editorial board and the founding editor of WORK: A Journal of Prevention, Assessment and Rehabilitation was conducted to examine the understanding of the editorship and contributions of this team to the knowledge in WORK. PARTICIPANTS: The team of four authors worked together to identify an approach to evaluate the contributions and impact of WORK's editorial board (EB) on the journal's scholarship. The editor-in-chief (EIC) and editorial board members were participants in this evaluation. METHODS: Informative and formative evaluations were used to investigate how knowledge was shaped through the development of an epistemic community of scholars in the field of work. Metrics of the EB composition and participation in the journal as well as surveys and interviews with the board and the editor-in-chief were analyzed. RESULTS: The EB represents an international community of scholars with a common interest in work and who contribute academically both within WORK and beyond. The epistemic community that has evolved through the editorial board represents a pluralistic perspective on work that is needed to inform practice, and knowledge. CONCLUSION: Future directions to continue to advance knowledge through WORK's editorial board and EIC are elaborated.
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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 | 0.067 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".