“How you keep going”: Voluntary sector practitioners' story‐lines as emotion work
Bibliographic record
Abstract
The voluntary sector acts as the last line of defense for some of the most marginalized people in societies around the world, yet its capacities are significantly reduced by chronic resource shortages and dynamic political obstacles. Existing research has scarcely examined what it is like for voluntary sector practitioners working amidst these conditions. In this paper, we explore how penal voluntary sector practitioners across England and Scotland marshaled their personal and professional resources to "keep going" amidst significant challenges. Our analysis combines symbolic interactionism with the concept of story-lines. We illuminate the narratives that practitioners mobilized to understand and motivate their efforts amidst the significant barriers, chronic limitations, and difficult emotions brought forth by their work. We position practitioners' story-lines as a form of emotion work that mitigated their experiences of anger, frustration, overwhelm, sadness, and disappointment, enabling them to move forward and continue to support criminalized individuals. Our analysis details three story-lines-resignation, strategy, and refuge-and examines their consequences for practitioners and their capacities to intervene in wicked social problems.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".