Surveillance in the System: Data as Critical Change in Higher Education
Bibliographic record
Abstract
Over recent decades, higher education infrastructures have become increasingly digitized and datafied. The COVID-19 pandemic accelerated adoption of online learning platforms, trading the walls of the classroom for digital systems. Yet the surveillance, privacy, and discrimination issues that such systems raise are minimally understood by those who teach and learn within them. This paper overviews a 2020 pilot survey and 2021-2022 qualitative study of higher education instructors on a global scale. These projects explored the ways in which instructors from various locales and academic status positions understand data and classroom tools using proxy questions surrounding knowledge, practices, experiences, and perspectives. This paper draws on those studies to frame concerns about datafication amplifying issues in higher education. Its premises are twofold: first, if higher education instructors, as knowledge workers, are not knowledgeable about the contexts within which they teach and conduct scholarship, then the construct of shared governance within higher education is inevitably undermined. Secondly, if faculty and academic decision-makers are not intentional about equitable and ethical use of digital platforms within higher education, students’ privacy and data is at risk. In this conceptual paper, we outline findings that frame datafication as a critical change within higher education culture.
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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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.117 |
| Scholarly communication | 0.032 | 0.049 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".