Senior Faculty Opinions on the Significance of Retirement Age - Employing Natural Language Processing
Bibliographic record
Abstract
This study is a pioneer study examining the significance of retirement in terms of lost investments and outcomes. Research findings on the output of academic faculty and on measures of excellence in higher education indicate that upon retirement the academic institution as an organization loses not only faculty who are still capable of contributing both to research and to teaching, but rather also two other important products: valuable knowledge and experience accumulated by senior faculty in the academic system in light of the institution’s investments in them. 107 questionnaires were collected from senior faculty members in a case study of one academic institution. A combined research method was utilized, consisting of qualitative and statistical analysis, with the aim of exploring the significance of retirement in terms of lost input and output, as perceived by academic faculty members. The research findings indicate that indeed, as perceived by the faculty, academic institutions as an organization lose faculty who are still capable of contributing to both research and teaching, as well as valuable knowledge and experience accumulated by senior faculty members within the academic system, after being nurtured by the academic institution.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".