Interns Talk Back: Disrupting Media Narratives about Unpaid Work
Bibliographic record
Abstract
In the 2010s, the intern rights movement challenged the view that the unpaid internship is an innocuous labour practice. This article draws on a review of international news coverage of unpaid internships between 2008 and 2015 and considers interviews with intern activists to document and contextualize the increasingly contentious issues at stake. Our analysis of media coverage identifies five recurrent media frames: employability, tough times, social mobility, legality, and backlash. While early news content tended to normalize unpaid work, unpaid internships were increasingly labelled as exploitative, illegal, and unfair. The coverage exhibited gaps and ideological strategies of containment, yet the case of unpaid internships appears to be an anomaly considering the difficulties that unions have tended to face in getting labour perspectives into mainstream media. We offer explanations for the extensive and often critical coverage of unpaid internships, highlighting media-savvy intern advocacy groups as particularly influential actors in shifting mainstream media narratives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".