Students at Work: Care work, Neoliberalism, and Survival in Precarious Times
Bibliographic record
Abstract
This research project examines the time use of undergraduate students at Queen’s University, a public university located on Anishinaabe and Haudenosaunee land in Ontario, Canada, with a focus on how much time they devote to care work. I argue that the university has become akin to a workplace, and students have accordingly become workers. This research is rooted in feminism theory and methodology, particularly intersectional feminist theory and social reproduction theory. I utilize time-use surveys and interviews to examine the potentially gendered and racialized dynamics of care work among students. I employ the feminist analytical approach of “studying up” by examining structural dynamics through the perspective of the everyday. I thus connect my findings to neoliberal theory and critique, its creation of so-called “life workers,” and the internalization of neoliberal discipline. I also discuss neoliberalism’s and the pandemic’s impact on care work and student time use overall. Finally, I use my interview findings to discuss structural issues at Queen’s University, and offer policy recommendations. I ultimately argue for the importance of centering care in a precarious world.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".