Work sucks, I know: Instagram as a platform for young people's labour grievances
Bibliographic record
Abstract
In Summer 2020, young workers from prominent Vancouver, British Columbia-based cafes, restaurants and breweries took to Instagram to air grievances about their workplaces. Precarious and violent working conditions in the food industry are business as usual in BC, which is reflected in the stories these workers shared of wage theft, unsafe workplaces, erratic scheduling, harassment and sexual violence. Held within the context of widespread layoffs in the industry due to the COVID-19 pandemic, these workers built communities of complaint and, in some cases, fundamentally changed the ownership and operations of their workplaces. To do this, these young workers navigated a complex web of digital/physical spaces and relationships to challenge abuses in their workplaces. By centering their complaints in the interrelationship between complaint, digital political protest, economic grievance and known forms of worker organizing, this paper explores how young people leveraged their grievances through their digital networks to influence their economic relationships and create safer workplaces for themselves and other workers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".