Researcher Identity in Transition: Signals to Identify and Manage Spheres of Activity in a Risk-Career
Bibliographic record
Abstract
Within the current higher education context, early career researchers (ECRs) face a ‘risk-career’ in which predictable, stable academic careers have become increasingly rare. Traditional milestones to signal progress toward a sustainable research career are disappearing or subject to reinterpretation, and ECRs need to attend to new or reimagined signals in their efforts to develop a researcher identity in this current context. In this article, we present a comprehensive framework for researcher identity in relation\nto the ways ECRs recognise and respond to divergent signals across spheres of activity. We illustrate this framework through eight identity stories drawn from our earlier research projects. Each identity story highlights the congruence (or lack of congruence)\nbetween signals across spheres of activity and emphasises the different ways ECRs respond to these signals. The proposed comprehensive framework allows for the analysis of researcher identity development through the complex and intertwined activities in which ECRs are involved. We advance this approach as a foundation for a sustained research agenda to understand how ECRs identify and respond to relevant signals, and, consequently, to unravel the complex interplay between signals and spheres of activity evident in struggles to become researchers in a risk-career environment. \nKeywords: researcher identity; identity development; signals; spheres of activity; risk-career
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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 | 0.037 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.008 |
| 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".