Enacting the Critical Potential of Occupational Science: Problematizing the ‘Individualizing of Occupation’
Bibliographic record
Abstract
Over the past decade, many occupational science scholars have emphasized the critical potential of occupational science; that is, its capacity to generate knowledge to inform practices that work against occupational inequities. Within this lecture, previously articulated concerns regarding the ‘individualizing of occupation’ within occupational science are politicized, by placing them within the broader ‘individualizing of the social’ that is associated with neoliberalism and related socio-political transformations. This broader ‘individualizing of the social’, which has involved configuring various social problematics as individual concerns and responsibilities within an array of social policies, discourses and practices, obscures the economic, political and other social factors that shape inequities in possibilities for work, retirement, education, leisure and other occupations. Working against such inequities requires problematizing the ‘individualizing of occupation’, within and outside of occupational science, and situating occupation within economic, political and other types of social forces. Drawing upon on-going research addressing the contemporary re-construction of retirement and later life work, I argue that critically examining how occupational possibilities are constructed in ways that align with broader socio-political forces, as well as how they are actively negotiated by individuals and collectives, provides a valuable way forward in enacting the critical potential of occupational science.
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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 | 0.063 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.027 | 0.196 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".