Commodification or Rationalization? Yes, please! Technology Transfer Talk in the Canadian Context
Bibliographic record
Abstract
There is much scholarship about recent changes in higher education, changes which to some extent appear to be globalized.This includes changes in the subjects that are researched and taught in universities in very different national contexts, like the widespread, relatively recent introduction of 'women's studies' in higher education institutions around the world.Similarly, it includes programmes selfconsciously seeking international convergence at the formal organizational level, like the European adoption of North American Bachelor, Masters, Doctorate model for higher education diplomas.Such transformations are discussed, planned, implemented and experienced in different ways across different national contexts and in varied higher education institutions with particular histories.Nonetheless, important cross-national commonalities may be observed in higher education institutions around the world.In this chapter, I examine proposed changes to one national university system -in Canada -from two perspectives, but with the assumption that the Canadian case speaks to changes in other national systems.By analyzing the same textual data from two different descriptive and analytical macrosociological approaches, one Marxist, the other Weberian, I seek to understand how theory shapes data analysis, that is, how different theoretical models highlight certain processes while making others invisible.What distinct, but arguably complementary, insights may be gained from Marxist and Weberian approaches, when applied to the same empirical object: the contemporary university?In the language of the title of this collection, how do these two theoretical models highlight the adoption of different, global
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".