Career studies in search of theory: the rise and rise of concepts
Bibliographic record
Abstract
Purpose – The purpose of this paper is to introduce further clarity to career scholarship and to support the development of career studies by complementing earlier theoretical literature reviews with an evidence-based historical analysis of career-related terms. Design/methodology/approach – Data from 12 career scholars were collected using the historical Delphi method to find consensus on the career terms that have shaped career studies between 1990 and 2012. The authors then explored the literature by collecting data on the occurrence of these terms, analyzing frequencies and trends via citations and indexes of citation using a mixed-method combination of historical literature review and performance analysis. Findings – Career scholarship is indeed a descriptive field, in which metaphors dominate the discipline. Career success and employability are basic terms within the field. The discipline tends to focus narrowly on career agents. There is a plethora of terminology, and, contrary to the expectations, concepts introduced tend not to fade away. Originality/value – The authors offer an overarching perspective of the field with a novel mixed-method analysis which is useful for theory development and will help unify career studies. Earlier comprehensive literature reviews were mostly based on theoretical reasoning or qualitative data. The authors complement them with results based on quantitative data. Lastly, the authors identify new research directions for the career scholarship community.
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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 | 0.058 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".