Transformational learning and engagement on climate action for students attending a climate negotiation
Bibliographic record
Abstract
Abstract When Greta Thunberg addressed world leaders at the United Nations Framework Convention on Climate Change (UNFCCC)’s 24th Conference of Parties (COP24), it highlighted how young people including Indigenous youth are seeking to influence international climate change negotiations. However, young people face barriers to effectively engaging in the COP processes with few opportunities to learn about the structure and practices for COP Observers. In this paper, we describe and evaluate a structured learning experience developed to support students conducting research related to climate change and their engagement with international climate negotiations. Before attending the COP24, students were given in-person and online training about the UNFCCC, its processes, and major issues under negotiation. They also developed and presented their work during a COP side event. Through pre- and post-surveys and in-depth interviews, we asked students about their expectations and degree of engagement and agency at the COP and more broadly on climate action. Students reported that the academic scaffolding before and during the COP provided most of the students with tools for navigating the complexities of the COP. For all of the students, learning through engagement with the COP24 process supported greater self-efficacy and literacy in relation to climate change action.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".