From global to local: advancing PRME at the local and regional levels
Bibliographic record
Abstract
This session introduces PRME Chapter UK and Ireland’s Local Networks, seven regional fora where PRME Signatories collaborate, share best practices, and engage with the PRME Principles and UN SDGs at a more localised level. The Local Networks’ success shows how PRME Chapters and individual Signatory schools can partner with academic institutions, business leaders, policymakers, and students to respond to local needs and challenges. Participants will gain (1) understanding of the benefits of addressing the Principles of PRME at more localised levels; (2) inspiration from examples of the PRME Chapter UK & Ireland Local Networks; (3) opportunities to share their stories of localising PRME and generate ideas for future development; and (4) post-event blog collating key insights from our session.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".