A new education agenda based on The International Science and Evidence Based Education Assessment
Bibliographic record
Abstract
The International Science and Evidence Based Education Assessment examined whether current education systems develop each person's full potential (aligned with the UN Declaration of Human Rights) and contribute to Sustainable Development Goal 4. Embracing a multidisciplinary approach, nearly 300 scientists from 45 countries conducted the assessment, calling for a shift in education's focus from economic growth to fostering human flourishing. Key findings included (a) the need for an integrative approach to learning, (b) moving beyond meritocracy and exploring potentiality as a better measure of student learning potential, and (c) using technology judiciously for scalable, equitable, and personalised learning. This paper seeks to highlight themes that were foundational to the assessment but not fully discussed within it. It advocates a global, transdisciplinary research agenda to close evidence gaps and inform policy to consider the complexity of the educational system and the need to think beyond existing conventions.
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 | 0.228 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.038 | 0.073 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.041 | 0.053 |
| Insufficient payload (model declined to judge) | 0.009 | 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".