Evaluating Academic Scientists Collaborating in Team-Based Research
Bibliographic record
Abstract
Criteria for evaluating faculty are traditionally based on a triad of scholarship, teaching, and service. Research scholarship is often measured by first or senior authorship on peer-reviewed scientific publications and being principal investigator on extramural grants. Yet scientific innovation increasingly requires collective rather than individual creativity, which traditional measures of achievement were not designed to capture and, thus, devalue. The authors propose a simple, flexible framework for evaluating team scientists that includes both quantitative and qualitative assessments. An approach for documenting contributions of team scientists in team-based scholarship, nontraditional education, and specialized service activities is also outlined. Although biostatisticians are used for illustration, the approach is generalizable to team scientists in other disciplines.The authors offer three key recommendations to members of institutional promotion committees, department chairs, and others evaluating team scientists. First, contributions to team-based scholarship and specialized contributions to education and service need to be assessed and given appropriate and substantial weight. Second, evaluations must be founded on well-articulated criteria for assessing the stature and accomplishments of team scientists. Finally, mechanisms for collecting evaluative data must be developed and implemented at the institutional level. Without these three essentials, contributions of team scientists will continue to be undervalued in the academic environment.
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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 | 0.360 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier 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".