Should dental school faculty be measured and compensated using academic productivity models? Two viewpoints
Bibliographic record
Abstract
Operationalizing faculty contributions in ways that align with organizational mission can be difficult, particularly when monetizing effort. Conventional compensation methods may result in faculty effort going undefined, resulting in more subjectivity in recognition and compensation. Inequities lead to faculty marginalization, fragmentation, decreased motivation, and attrition. Dental faculty retirements are expected to increase, as 81% of men and 19% of women faculty aged 60 years and older in 2015-2016. We present opposing perspectives on the use of educational value units (EVUs) in academic dentistry. The first viewpoint articulates that such models improve recruitment and retention by objectifying (a) faculty performance measurement, (b) academic productivity improvements, and (c) compensation determination. The counterpoint suggests EVUs are deterrents to faculty retention due to challenges with objectively quantifying performance measures, a potential inherent bias linked to gender, and the undervaluing of teaching quality or collaborative practices.
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 | 0.072 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".