Dr Jacqueline Baxter, Editor in Chief, interviews Dr Tracey Burns of the OECD about the impact of COVID-19 on education across OECD countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<i><b>Tracey Burns</b></i> 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. <i><b>Jacqueline Baxter</b></i> in conversation with Tracey Burns.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it