Surveillance practices, risks and responses in the post pandemic university
Bibliographic record
Abstract
This paper describes and critiques how surveillance is situated and evolving in higher education settings, with a focus on the surveillance of teaching and learning. It argues that intensifying practices of datafication and monitoring in universities echo those in broader society, and that the Covid-19 global pandemic has both exacerbated these practices and made them more visible. Surveillance brings risks to learning relationships, academic and work practices, as well as reinforcing economic models of extraction and inequalities in education and society. Responses to surveillance practices include resistance, advocacy, education, regulation and investment, and a number of these responses are examined here. Drawing on scholarship and practice, the paper provides an in-depth overview of this topic for people in university settings including those in leadership positions, learning technology roles, educators and students. The authors are part of an international network of researchers, educators and university leaders who are working together to develop new approaches to surveillance futures for higher education: https://aftersurveillance.net/. Authors are based in Canada, South Africa, the United Kingdom and the United States, and this paper reflects those specific contexts.
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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| 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".