Introduction to the special issue: Education privatisation and commercialisation in the context and aftermath of the COVID-19 pandemic
Bibliographic record
Abstract
While different forms of privatisation and commercialisation of education have existed since at least the 1990s, the global COVID-19 pandemic of 2020-2022 created opportunities for the integration of private and commercial interests in public education to be turbo-charged. In this introduction to the special issue, we place its articles featuring country case studies from Italy, Brazil, and the United Kingdom in context, providing some historical and conceptual background to the forms, impacts and pathways of privatisation and commercialisation of education. We argue, based on our reading of the three articles in this collection and of the literature more broadly, that the education crisis brought about by the global pandemic swiftly opened the doors to new private actors and new forms of public-private partnerships. Due to the enduring nature of many of the relationships established over the course of the crisis, we observe the longer-term impacts in the form of expanded and intensified reach of the global education industry and more entrenched forms of privatisation and commercialisation of education in many contexts worldwide.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".