Architectures of assetization: Legacy infrastructures and the configuration of datafication in UK higher education
Bibliographic record
Abstract
We outline the concept of ‘architectures of assetization’ as a way to get at the political-economic configuration of datafication in higher education through the layering of educational technology (‘edtech’) onto existing, legacy infrastructures. Edtech provides a useful empirical object of study because of the increasing deployment of new digital technologies in educational organizations; our focus is on higher education institutions (i.e. universities) in the United Kingdom. The empirical analysis is split between a discussion of digital infrastructures and architectures of (data) assetization in higher education; the tensions arising between new digital infrastructures and legacy infrastructures in UK higher education institutions; and the implications of reconfiguring legacy infrastructures for UK universities. We pay particular attention to the creation of new techno-economic objects, especially the transformation of personal and user data into an asset, as datafication transforms higher education in unexpected and not necessarily beneficial ways.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".