Getting through career crises: Insights from history, philosophy and research
Bibliographic record
Abstract
Getting through career crises: Insights from history, philosophy and researchPerpetually struggling for that elusive life-work balance?Got a job, then lost a job?Missed out on your next career goal?Told your performance needs managed?Feel abjectly disconnected from your workplace and those in it?Serially rejected from everything important you touch?Our careers and work can go awry in so many different ways.Whether related to work focus, decisions, or outcomes -career setbacks can be minor annoyances but on other occasions can leave us reeling: questioning fundamental aspects of ourselves, our life in academia and, at its worse, whether life can even go on.Not unlike grieving (Kubler-Ross 1979),crises often comes with shifting denial, anger, bargaining, and depression-even bereavement (Brown 2015).Notably, no one is immune: career crises can happen at any stage and bring a wide range of associated mental health challenges for students (Evans et al. 2018, Nature Editorial 2018) and seasoned professors alike (Guthrie et al. 2017). The nature and challenges of academic workOur common responses to career crises are understandable.For example, research indicates that in response to failure, it's common to get defensive, reduce aspirations or ambition, plan to play or even cheat the system next time into your favour, or
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".