Digital Scholarship in the University Tenure and Promotion Process: A Report on the Sixth Scholarly Communication Symposium at Georgetown University Library
Bibliographic record
Abstract
Four notable scholars who have done significant work in digital scholarly projects were invited to speak on the theme of ‘digital scholarship in the university tenure and promotion process’ at Georgetown University Library to explore the scholarship and the continuing problems with evaluating it – particularly for promotion and tenure. There were significant similarities in the critiques (promotion and tenure committees are hidebound and/or lack the expertise to understand and evaluate these scholarly products), but there were also differences in the assessment of the current situation on the role and importance of technological tools, the nature of scholarship among the disciplines, and the variety of models available for academe to adapt to address these issues. The discussion explored aspects such as the role of trade publishing, reinstating the canon, and the nature of credit and reward for such projects.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".