Online Remote Proctoring Software in the Neoliberal Institution: Measurement, Accountability, and Testing Culture
Bibliographic record
Abstract
As COVID-19 spread in early 2020, a lockdown was implemented across Canadian provinces andterritories, resulting in the shuttering of physical post-secondary campuses. Universities quicklypivoted to remote learning, and faculty members adjusted their instructional and assessmentapproaches to align with virtual environments. Presumably to aid with this process, a number ofinstitutions acquired licenses to remote online proctoring services. This paper examines theresearch around online remote proctoring, examining the justification offered for the adoption ofonline remote proctoring, and contemporary research on assessment practices in higher education.Throughout the paper, I demonstrate a lack of research that speaks to the efficacy of this mode ofassessment while also acknowledging shifts in the testing environment, and an increase in studentanxiety. I argue that online remote proctoring is not only embedded within neoliberalism and auditculture, but supports a continued reliance on testing culture. It concludes with a discussion ofassessment culture, offering some alternative assessment approaches that might disrupt the veryneed for online remote proctoring. Keywords: Online remote proctoring, assessment, testing
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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 | 0.046 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".