Dr Jacqueline Baxter, Editor in Chief, interviews Dr Tracey Burns of the OECD about the impact of COVID-19 on education across OECD countries
Bibliographic record
Abstract
Tracey Burns is a Senior Analyst in the OECD’s Centre for Educational Research and Innovation. She heads a portfolio of projects including Innovative Teaching for Effective Learning, 21st Century Children and Trends Shaping Education. Until recently she was also responsible for the OECD work on Governing Complex Education systems. Previous to her time at the OECD she worked on social determinants of health and well-being. As a Postdoctoral Fellow at The University of British Columbia, Dr Burns led a research team investigating newborn infants’ responses to language and was an award-winning lecturer on infant and child development. She is the recipient of numerous awards and honours, including The University of British Columbia Postdoctoral Fellowship and the American Psychological Association Dissertation Research Awards. Tracey holds a BA from McGill University, Canada, and an MA and Doctor of Philosophy in psychology from Northeastern University, USA. Jacqueline Baxter in conversation with Tracey Burns.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".