A look at public engagement, publication outputs and metrics in the tenure review process
Bibliographic record
Abstract
Presented October 11, 2018 at FORCE 2018. After revising the policy guidelines that inform the tenure review process in 129 institutions of higher education across the United States and Canada, an interdisciplinary team of researchers asked this question: Are we serving the public, or are we serving ourselves? Our ongoing research project revised 864 documents and forms that guide the promotion, tenure and review process in several Canadian and American institutions to identify the mentions to public and community engagement in research and scholarly work. We found that, although there are high levels of broad interest in public and community engagement in scholarship, such an interest is not precisely aligned with the specific scholarly outputs required from faculty, and the metrics for evaluating publication impact. Thus we would like to discuss with the academic community: How should we transform these guidelines, and the overall tenure review process, to ensure that public and community engagement in scholarship becomes a more meaningful requirement in faculty promotion and evaluation?
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 | 0.346 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.057 | 0.025 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".