Are contemporary notions of academic career progression ‘fit-for-purpose’? Evidence for a new framing of (academic) careers
Bibliographic record
Abstract
Of what value are institutionally rooted terms notions like ‘mid-career’ or ‘early career’ academic? I argue here we need a more expansive perspective on careers than the linear institutional process represented in such terms as they as do not represent the reality of contemporary academia. This argument is rooted in the evidence emerging from our close to 20 years of narrative methodology research which demonstrated that today’s (academic) career progression can better to understood as a rich contextually embedded set of experiences in which individuals self-author and self-define their careers across organisations, time and space. As already noted, terms like ‘mid-career’ draw on a no longer existing career pattern. Second, this traditional framing narrowly focuses on academic work alone, rather than situating work within the influence of individual biography: how work is embedded within life, with work-life decisions intertwined. Third, we need to place individual experience within the broader historical and contemporary socio-economic affordances and constraints that influence careers. Such influences – for instance these days, greater mobility and more accountability – are in constant flux. These results provide an alternate view that offers a firmer and more expansive foundation for today’s Master’s, PhDs and graduates to develop their career literacy.
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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 | 0.030 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".