Centering the Complexity of Long-Term Unemployment: Lessons Learned from a Critical Occupational Science Inquiry
Bibliographic record
Abstract
Inquiries that rely on temporal framings to demarcate long-term unemployment risk generating partial understandings and grounding unrealistic policy solutions. In contrast, this four-phase two-context study aimed to generate complex understandings of post-recession long-term unemployment in North America. Grounded in a critical occupational perspective, this collaborative ethnographic study also drew on street-level bureaucracy and governmentality perspectives to understand how social policies and discursive constructions shaped people’s everyday ‘doing’ within the arena of long-term unemployment. Across three phases, study methods included interviews with 15 organizational stakeholders who oversaw employment support services; interviews, participant observations, and focus groups with 18 people who provided front-line employment support services; and interviews, participant observations, time diaries, and occupational mapping with 23 people who self-identified as being long-term unemployed. We draw on selected interviews and mapping data to illustrate how participants’ definitions and experiences of long-term unemployment reflected and moved beyond dominant temporally based framings. These findings reinforce the need to expand the dominant conceptualizations of long-term unemployment that shape scholarly inquiries and policy responses. Reflections on the benefits and challenges of this study’s design also reinforce the need to use multiple, flexible methods to center the complexity of long-term unemployment as it is experienced in everyday life.
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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 | 0.066 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.071 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".