Authors' opinions on publication in relation to annual performance assessment
Bibliographic record
Abstract
BACKGROUND: In the past 50 years there has been a substantial increase in the volume of published research and in the number of authors per scientific publication. There is also significant pressure exerted on researchers to produce publications. Thus, the purpose of this study was to survey corresponding authors in published medical journals to determine their opinion on publication impact in relation to performance review and promotion. METHODS: Cross-sectional survey of corresponding authors of original research articles published in June 2007 among 72 medical journals. Measurement outcomes included the number of publications, number of authors, authorship order and journal impact factor in relation to performance review and promotion. RESULTS: Of 687 surveys, 478 were analyzed (response rate 69.6%). Corresponding authors self-reported that number of publications (78.7%), journal impact factor (67.8%) and being the first author (75.9%) were most influential for their annual performance review and assessment. Only 17.6% of authors reported that the number of authors on a manuscript was important criteria for performance review and assessment. A higher percentage of Asian authors reported that the number of authors was key to performance review and promotion (41.4% versus 7.8 to 22.2%). compared to authors from other countries. CONCLUSIONS: The number of publications, authorship order and journal impact factor were important factors for performance reviews and promotion at academic and non-academic institutes. The number of authors was not identified as important criteria. These factors may be contributing to the increase in the number of authors per publication.
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 | 0.066 | 0.336 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".