Turning universities into data-driven organisations: seven dimensions of change
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
Abstract Universities are striving to become data-driven organisations, benefitting from data collection, analysis, and various data products, such as business intelligence, learning analytics, personalised recommendations, behavioural nudging, and automation. However, datafication of universities is not an easy process. We empirically explore the struggles and challenges of UK universities in making digital and personal data useful and valuable. We structure our analysis along seven dimensions: the aspirational dimension explores university datafication aims and the challenges of achieving them; the technological dimension explores struggles with digital infrastructure supporting datafication and data quality; the legal dimension includes data privacy, security, vendor management, and new legal complexities that datafication brings; the commercial dimension tackles proprietary data products developed using university data and relations between universities and EdTech companies; the organisational dimension discusses data governance and institutional management relevant to datafication; the ideological dimension explores ideas about data value and the paradoxes that emerge between these ideas and university practices; and the existential dimension considers how datafication changes the core functioning of universities as social institutions.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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