Learning to labor in high-technology: experiences of overwork in university internships at digital media firms in North America
Bibliographic record
Abstract
Long working hours have become a normal and expected characteristic of employment in many sectors in the Global North. In this paper I examine subjective and affective experiences of overwork that define students’ discussions of internships pursued as mandatary aspects of cooperative undergraduate degree programmes. I interviewed current and former students at the University of Waterloo who completed internships at digital media firms, the majority of whom experienced overwork at these firms. Internships are settings in which young people’s expectations of employment begin to solidify, while digital media jobs are often considered particularly desirable – evidence of successful employment at the apex of a globalized and competitive labor market. I argue that exploring experiences of overwork shows how and why overwork has been and continues to be normalized, while radical alternatives to overwork (e.g., work refusal and anti-work politics) become hard to imagine and enact.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 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".