Evaluating Academic Scientists Collaborating in Team-Based Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it