Resisting Common-sense Notions of Employability, Risk, and Race: A Multi-case Study of Reimagined Approaches to Youth Employment Training in Toronto
Bibliographic record
Abstract
This study examines how community-based programs for youth are resisting an ‘employability agenda’ to overcome, using a Gramscian analysis, ‘common-sense’ approaches to youth employment training in racialized communities. These common-sense approaches run the risk of treating youth as human capital to be supplied to the market in low-wage positions. Such approaches focus principally on securing employment for young people, divorced from their social context and aspirations, without regard for the development of capacities and skills that assist youth over time and in other aspects of their lives. Through a qualitative multi-case study, I examine three Toronto youth programs to identify alternative and potentially transformative approaches that shift the focus away from job attainment only, towards a broader vision of individual progress that includes community progress. I find that program staff formulate a coherent vision of youth situated within their social reality that enables youth to advance in their education, work, and family life and to contribute to their community in an integrated fashion. Four key elements emerge in assisting youth to move beyond employability: integrating programmatic aims instead of fragmenting them; a conversational approach as opposed to an overreliance on training; mentorship that is reciprocal in nature; and service in one’s community as a practical expression of advancing community and individual well-being simultaneously. When these elements work in concert, program staff support youth to ‘decenter’ employment as the main objective and purpose of life, aided by the power of their own example and conceptually clarifying conversations. The community-rootedness of the programs is significant in several ways. First, it protects staff from falling into the approaches of the employability and risk discourse propagated by conventional training programs. Second, the neighbourhood as a social space or context is a more developmentally appropriate setting for building employment-related skills and capacities than precarious low-skill, low-wage jobs that are part of traditional employment training placements. Finally, communities offer a context for young people to abandon the transactional relationships underpinning their commodification as economic subjects and instead establish transformational relationships with others that benefit individual and community well-being.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".