“Activated, but Stuck”: Applying a Critical Occupational Lens to Examine the Negotiation of Long-Term Unemployment in Contemporary Socio-Political Contexts
Bibliographic record
Abstract
Background: Solutions for the problem of long-term unemployment are increasingly shaped by neoliberally-informed logics of activation and austerity. Because the implications of these governing frameworks for everyday life are not well understood, this pilot study applied a critical occupational science perspective to understand how long-term unemployment is negotiated within contemporary North American socio-political contexts. This perspective highlights the implications of policy and employment service re-configurations for the range of activities that constitute everyday life. Methods: Using a collaborative ethnographic community-engaged research approach, we recruited eight people in Canada and the United States who self-identified as experiencing long-term unemployment. We analyzed interviews and observation notes concerning four participants in each context using open coding, critical discourse analysis, and situational analysis. Results: This pilot study revealed a key contradiction in participants’ lives: being “activated, but stuck”. This contradiction resulted from the tension between individualizing, homogenizing frames of unemployment and complex, socio-politically shaped lived experiences. Analysis of this tension revealed how participants saw themselves “doing all the right things” to become re-employed, yet still remained stuck across occupational arenas. Conclusion: This pilot study illustrates the importance of understanding how socio-political solutions to long-term unemployment impact daily life and occupational engagement beyond the realm of job seeking and job acquisition.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.032 | 0.072 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".