Life after tenure: Professional development strategies for mid‐career faculty
Bibliographic record
Abstract
ABSTRACT Many publications, institutional policies, and resources focus on the professional development of doctoral students and junior faculty while professional development needs and resources available to mid‐career faculty receive limited attention. The proposed panel aims to facilitate a discussion on issues faced by mid‐career faculty in the information disciplines. Professional development resources and strategies currently available to mid‐career faculty and administration will also be discussed. The panelists will include mid‐career and senior faculty from various institutions and information disciplines who will bring their multi‐disciplinary and international perspectives to the panel. The panel will be of interest to faculty and academic administrators at all‐levels of their careers.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".