The Datafied School in the Neoliberal Era: Pandemic Shifts in South Korean Education Policy
Bibliographic record
Abstract
COVID-19 was a critical juncture for education. Powerful tech corporations seized the opportunity to “blitzscale” (Hoffman & Yeh, 2018) data-driven education technologies and push business-friendly policies and infrastructure (Williamson, 2021). Focusing on the case of South Korea, I argue that its pandemic-era policies on “AI textbooks” conflict with public values of education and worked to (1) frame education primarily as an optimization of human capital enhancement for state modernization, (2) further subjugate an already politically vulnerable education sector to technocentric solutions, and (3) consolidate a theory of education driven by techno-utopianism, which generates an important gap between the “perfect” imaginaries and actualities. These shifts add up to a neoliberal vision of the datafied school, in which longstanding debates around “better” education are ostensibly resolved through artificial intelligence and algorithmic technologies ranging from pervasive student surveillance, predictive analytics of student performance, and to hidden commodification of children’s everyday data.
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.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| 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".