Data as asset, data as rent? Rentiership practices in EdTech startups
Bibliographic record
Abstract
The Covid pandemic highlighted the increasing deployment of digital technologies in educational institutions, defined as ‘edtech’. The most visible edtech was video conferencing software, but a swathe of edtech startups have sought to roll out their products and services to educational institutions. We focus specifically on the deployment of edtech in UK higher education by these startups. Much of this deployment happens behind the scenes with little public debate, raising concerns about the implications of the increasing digitalization of higher education. Of particular concern, edtech startups have significantly expanded their data collection capacities and analytics through the roll-out of edtech across universities. Data are being transformed into assets (i.e., capitalizable property) by these startups, promising to generate economic rents for them. Examining this data assetization in the edtech sector enables us to analyze what kinds of data rents are being created from what kinds of data assets.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".