Assessment or Surveillance? Panopticism and Higher Education
Bibliographic record
Abstract
In this paper, we explore Jeremy Bentham’s panopticon and consider its possible contribution to understanding more fully the impact of current assessment protocols and practices within higher education. More pointedly, we ask the following question: Are the plethora of assessment practices within higher education actually designed to improve student academic experience, or are they instead mechanisms of surveillance intended to control, dominate and invoke paranoia among university workers? In response to this question, we argue that the prevailing preoccupation with assessment in U.S. universities is motivated less by a genuine desire to enhance the actual academic experience of students than it is to offer hegemonic interests a psychological instrument of control to eliminate potential dissent over neo-liberal and managerial class imposed policies. To illustrate our central claim, we first review some general details of Bentham’s panopticon. Secondly, we briefly discuss Foucault’s analysis of the panopticon and consider his accompanying postulate on the tendency of individuals to self-regulate their behavior according to externally imposed and monitored expectations. We also examine in greater depth the relationship between the panopticon and the Lacanian gaze, with a focus on considering the latter’s psychological impact. Finally, we contend that the prevailing obsession with assessment in universities is a mechanism of institutional control that hinders rather than enhances the academic quality of contemporary higher education by limiting the scope of academic dialogue, social imagination and democratic structural critique.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".