Ontario Interns Fight Back: Modes of Resistance Against Unpaid Internships
Bibliographic record
Abstract
In this article I report on three ways that interns and those sympathetic to their plight are opposing unpaid internships, focusing on the Canadian province of Ontario as a case. First, I analyze the ways that interns engage in social activism to raise awareness about problems with unpaid internships. Second, I examine several lawsuits that interns have waged against companies in an attempt to secure back pay. Third, I analyze the Ontario Ministry of Labour’s response to the growing concerns surrounding unpaid internships, and recent proposals that aim to strengthen governmental regulations. Arguing that possibilities for change have arisen largely due to the efforts of interns themselves, I conclude each section by noting some of the strengths and limitations afforded by each type of resistance.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.026 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".