ACES - A Community-centred Educational Model for Developing Social Resilience
Bibliographic record
Abstract
The OECD (2018) emphasises that to navigate an increasingly uncertain and ambiguous world, young people will need to develop curiosity, imagination, resilience, and self-regulation. In developing countries such as Malaysia, Indonesia, and Vietnam, efforts to maintain sustainable growth in their respective economies hinges on a progressing and updated system of education that is more transformational (Ilie & Rose, 2016). In this symposium we will present findings to date from ACES - A Community-centred Educational Model for Developing Social Resilience, funded by the UKRI-ESRC under the Global Challenges Research Fund, led by academics at a UK University. Across the four papers the project partners will discuss how ACES has engaged with young people and teachers in experimenting with playful education for facilitating localised creative thinking, problem-solving and frugal learning.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".