Bibliographic record
Abstract
Abstract Objectives The number of academics reaching traditional retirement age has been increasing thus motivating the need to better understand retirement experiences among higher education faculty. This review paper aims to: (i) identify the types of studies and traits being measured in studies of academic retirement, (ii) understand the determinants of retirement, including pathways and obstacles, among academics being measured in the literature, (iii) map the most prevalent types of studies and findings regarding academic retirement, and (iv) appraise the current state of the literature. Methods We conducted a systematic review of the literature to address our research objectives. Studies were identified from multiple database using search terms regarding retirement among academic faculty. Included studies examined determinants of academic retirement, perspectives, and/or retirement experiences among academics. Four reviewers screened the studies for inclusion and completed the data extraction. Results A total of 102 papers met the inclusion criteria. Most studies were based on cross-sectional survey data, followed by qualitative interviews; quantitative, qualitative, and mixed-methods research were included. Commonly measured determinants included age and spousal retirement timing. A mapping of the literature indicates a focus on geographical and/or subfield areas. Conclusions: Insights regarding academic’s retirement experiences provide insights about varying retirement pathways and can help elucidate retirement pathways that enhance transitions particularly for those in professions with generally high autonomy. Future research ought to focus on supports to help retain mature workers within academia and overall determinants of satisfaction with retirement among academics.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".