Games academics play and their consequences: how authorship, <i>h</i> -index and journal impact factors are shaping the future of academia
Bibliographic record
Abstract
Research is a highly competitive profession where evaluation plays a central role; journals are ranked and individuals are evaluated based on their publication number, the number of times they are cited and their h -index. Yet such evaluations are often done in inappropriate ways that are damaging to individual careers, particularly for young scholars, and to the profession. Furthermore, as with all indices, people can play games to better their scores. This has resulted in the incentive structure of science increasingly mimicking economic principles, but rather than a monetary gain, the incentive is a higher score. To ensure a diversity of cultural perspectives and individual experiences, we gathered a team of academics in the fields of ecology and evolution from around the world and at different career stages. We first examine how authorship, h -index of individuals and journal impact factors are being used and abused. Second, we speculate on the consequences of the continued use of these metrics with the hope of sparking discussions that will help our fields move in a positive direction. We would like to see changes in the incentive systems, rewarding quality research and guaranteeing transparency. Senior faculty should establish the ethical standards, mentoring practices and institutional evaluation criteria to create the needed changes.
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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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.033 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".