Connecting Public Policies and Everyday Activities via Mobilizing an Occupational Perspective
Bibliographic record
Abstract
Statement of Purpose: The need to bring an occupational perspective to bear within policy and public spheres has increasingly been framed as a “duty” of occupational scientists. However, given the dominance of a market model of society and its neoliberal view of humans as economic and entrepreneurial beings, how can occupational scientists ensure that their work “intensifies the value of research by providing a new lens through which public policy data can be interpreted” (Urbanowski, Shaw, & Chemuttut, 2013, p. 315)? This poster presentation describes knowledge mobilization efforts for a two-site, community engaged, collaborative ethnographic (Lassiter & Campbell, 2010) study of long-term unemployment in the United States and Canada that has been conducted since 2014.\nMethods: To understand possibilities and boundaries for occupational engagement within the situation of long-term unemployment, we generated data at three levels in the United States and Canada: we interviewed 15 organizational stakeholders and reviewed organizational documents; we interviewed and observed 18 front-line employment support service providers; and we interviewed, observed, and completed time diaries and/or occupational maps with 23 people who self-identified as being long-term unemployed. Data analysis approaches included situational analysis (Clarke, 2005), critical discourse analysis (Cheek, 2004), and critical narrative inquiry (Hardin, 2003).\nResults: Many of our findings illustrate the ways in which personal, environmental, material, non-material, and discursive situational elements create experiences of being “stuck” in long-term unemployment. To mobilize these findings beyond the academic realm, we are writing a series of site summaries and issue briefs that we can use to communicate with stakeholders and policy makers in each study context. These documents, along with other information about the study, are also being catalogued on a project website. Finally, we are planning knowledge mobilization workshops that will not only disseminate findings but will also bring study participants and policy makers together in an effort to minimize future experiences of being “stuck” in long-term unemployment.\nImplications: By engaging participants in a discussion of non-academic knowledge mobilization efforts, we hope to strengthen disciplinary commitments to make occupational science research useful outside the academic realm.\nDiscussion questions: What modes of non-academic knowledge mobilization might be used in occupational science? In what ways can policy makers, in particular, be best engaged by researchers to foment change? \nKey words: Long-term unemployment, critical qualitative research, knowledge mobilization
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.047 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".