‘It is intimidating going into your first job’: Young teens and workplace safety
Bibliographic record
Abstract
Abstract Drawing on qualitative focus groups and interviews, this paper draws on participatory and relational approaches to explore how prospective and very new workers in their early teens in Canada talk about and navigate workplace safety. We foreground our young participants' discussions and safety management strategies to discuss their shared and sometimes narrow understanding of unsafe work; their mixed, and often limited, experiences of safety training; and the individualized avenues they prioritize to deal with potential and concrete safety issues. Countering our participants' inclination towards individualized solutions, we focus on how these young workers are embedded in relationships with others as well as the material world, including workplace cultures; networks and hierarchies of people; specific materials, time and time pressures; and safety‐related policies. Such a relational lens can in turn guide how we think about fostering workplace safety in ways that challenge more individualized approaches, specifically through recognizing interdependencies that shape how young people think about workplace safety; expanding beyond unidirectional, individualized educational strategies; and favouring shared self‐advocacy, especially with older workers, including through unionization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".