4. Imagining higher education as infrastructures of care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter is both retrospective and prospective. The authors contend that universities are constitutive of extractive infrastructures in the context of increased reliance on corporate controlled digital platforms, and processes that render faculty and students data objects rather than active agents of education. Using pertinent and powerful examples, the argument is made that universities are framed by extractive infrastructures that encode logics of ownership, competition, individualism and commodification to reproduce inequalities. This framework is extended to pervasive data collection practices inherent in learning management platforms, performance measurement systems, university rankings, and other technologies that inform higher education discourses, policies and practices. This prompts imagining otherwise by conceiving universities as infrastructures of care. The authors offer a remaking of the “good” university by creating material, epistemic and affective structures that operate on principles and values of reciprocity, reparation, gifting, sovereignty, hospitality, and epistemic pluralism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it