The evaluation of scholarship in academic promotion and tenure processes: Past, present, and future
Bibliographic record
Abstract
Review, promotion, and tenure (RPT) processes significantly affect how faculty direct their own career and scholarly progression. Although RPT practices vary between and within institutions, and affect various disciplines, ranks, institution types, genders, and ethnicity in different ways, some consistent themes emerge when investigating what faculty would like to change about RPT. For instance, over the last few decades, RPT processes have generally increased the value placed on research, at the expense of teaching and service, which often results in an incongruity between how faculty actually spend their time vs. what is considered in their evaluation. Another issue relates to publication practices: most agree RPT requirements should encourage peer-reviewed works of high quality, but in practice, the value of publications is often assessed using shortcuts such as the prestige of the publication venue, rather than on the quality and rigor of peer review of each individual item. Open access and online publishing have made these issues even murkier due to misconceptions about peer review practices and concerns about predatory online publishers, which leaves traditional publishing formats the most desired despite their restricted circulation. And, efforts to replace journal-level measures such as the impact factor with more precise article-level metrics (e.g., citation counts and altmetrics) have been slow to integrate with the RPT process. Questions remain as to whether, or how, RPT practices should be changed to better reflect faculty work patterns and reduce pressure to publish in only the most prestigious traditional formats. To determine the most useful way to change RPT, we need to assess further the needs and perceptions of faculty and administrators, and gain a better understanding of the level of influence of written RPT guidelines and policy in an often vague process that is meant to allow for flexibility in assessing individuals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.441 | 0.578 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".