Out of the Shadows: The Global Intensification of Supplementary Education
Bibliographic record
Abstract
Researching supplementary education: Plans, realities, and lessons from fieldwork in china. The insecurity industry: Supplementary education in Japan. Supplementary education in turkey: Recent developments and future prospects. Private tutoring in vietnam: A review of current issues and its major correlates. Supplementary education in brazil: Diversity and paradoxes. Supplementary education in a changing organizational field: The canadian case. But did it help you get to university? A qualitative study of supplementary education in western australia. Supplementary education in the United States: Policy context, characteristics, and challenges. Supplementary education in germany: History and present developments. Making markets: Policy construction of supplementary education in the united states and korea. Family capital: A determinant of supplementary education in 17 nations. Out of the Shadows: The Global Intensification of Supplementary Education. International Perspectives on Education and Society. Out of the Shadows: The Global Intensification of Supplementary Education. Copyright page. List of Contributors. Foreword. Out of the shadows? An introduction to worldwide supplementary education. About the Authors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".