After the Crisis: Explaining stories of professional identity growth from collective action
Bibliographic record
Abstract
Emergent forms of collaboration are central to societies' response to crises like natural disaster, refugee migration and pandemics. Even though individuals' participation in such collective action may be short-lived, recent studies propose it can inspire enduring professional role change as people return to their everyday work post-crisis. Yet, previous research does not focus on the divergent stories participants tell about the crisis years afterward and what it meant for them professionally. Through a grounded study of healthcare workers involved in the 2003 Toronto SARS outbreak, we examine varied narratives of professional identity growth following collective action in crisis. Years after SARS, participants told diverse stories about the crisis as an event that suspended, affirmed or even expanded their professional identities. Participants with narratives of identity suspension saw SARS as an event lacking professional relevance. Narratives of identity affirmation and expansion, however, emphasized growth and inspiration for participants' professional roles post-crisis. We theorize how interactions within collective responses can foster growth narratives, when they enhance meanings central to participants' professional identities and by affording follow-on interactions that translate these meanings into role change. We contribute new insight on how collective action in crisis can lead to professional role change post-crisis, how fragmented perspectives affect capacity for collective action in intermittent crises, and the role of follow-on interactions in professionals' narrative identity work.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".