The Value of Quality: Capital, Class, and Quality Assessment in the Re-making of Higher Education in the United State, the United Kingdom, and Ontario
Bibliographic record
Abstract
This dissertation examines the utility of quality assessment (QA) in higher education as a means of measuring and improving qualitative excellence. It also tracks the emergence and development of QA in the United States, the United Kingdom, and Ontario. I find that QA neither measures nor helps to produce anything that could meaningfully be described as being of high “quality”. Rather, QA is effective in helping to reproduce commercially oriented but hardly ground-breaking research and a more “flexploitable” labour force. \n\tThe precursors to contemporary forms of QA first appeared in United States during the early part of the 20th century. To serve the interests of a burgeoning capitalism, corporate America organized independently and under the aegis of the American state to develop and control a national system of higher education. To that end, the captains of industry developed an extensive program of measurement and evaluation as a basis to rationalize funding for university teaching and research. Over time, that system of measurement and assessment developed into what today appears as a massive network of procedures and metrics that aid in the reproduction of a stratified system of higher education that efficiently puts out the kinds of knowledge and workers that can in turn aid in the reproduction of neoliberal capitalism. \n\tSince 1980, successive governments in both the United Kingdom and Ontario have developed systems of QA in the hope of reproducing the kinds of results achieved in America. QA has been seen as a way to install a price-type signaling system, and thereby a market, in what are subsidized and public systems of higher education. In other words, systems of QA were developed to evaluate the exchange-value of new knowledge and graduates within the context of neoliberal capitalism. Accordingly, QA makes it possible for firms and the state to rationalize funding in a manner that disciplines those within and around the university – increasingly by consent - to produce a particular form of value, namely that which can help corporations to secure larger profits, irrespective of the social, political, economic, or ecological consequences.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".