Development of GreenDealz: A public engagement toolkit addressing critical raw materials and the EU Green Deal at informal education settings
Bibliographic record
Abstract
Abstract. One of the most important challenges that Europe faces to date is the need for a sharp increase in the extraction, production and recycling of critical raw materials to meet the demands of renewable energy technologies as specified in the European Union’s (EU) climate targets. However, this topic is not widely discussed amongst publics and is underrepresented within the field of informal public engagement. We present the development of a public engagement toolkit called ‘GreenDealz’ that addresses this gap. We focus specifically on informal learning within the festival environment. GreenDealz was created via an iterative process informed by festival-based data collection and audience input. GreenDealz engages incidental audiences with a supermarket experience, where critical raw materials must be shopped for to build key renewable energy technologies and achieve EU climate goals. Evaluation is streamlined into the tactile experience of GreenDealz, employing embedded assessment measures which yield quantitative data that indicate this activity significantly enhances audience knowledge of this topic.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".