Evaluating the impact of a youth polar expedition alumni programme on post-trip pro-environmental behaviour: a community-engaged research approach
Bibliographic record
Abstract
Youth-based programmes providing education-based expeditions to the Polar Regions have been offered for more than two decades, and whilst studies hint that participants return as inspired and empowered ambassadors, research to date has been inconclusive as to what impact such expeditions have had on their participants’ subsequent lifestyle decisions and pro-environmental behaviours. To address this research gap, Social Identity Theory (SIT) was used to evaluate the impact of youth polar expeditions on participants’ pro-environmental behaviour, up to 18 years after their polar voyage. In collaboration with Students on Ice (SOI), this study tested the direct and indirect relationships between previous SOI students’ (n = 217) social identity towards the alumni programme and their subsequent connections with nature and pro-environmental behaviours. Findings suggest that social identity might be one way to explain the long-term impact of educational expeditions in terms of desired future pro-environmental behaviours, underscoring the critical importance of an alumni programme. Furthermore, a Community-Engaged Research (CER) approach was adopted to evidence the impact of this research beyond the realm of academia. We reflect on the CER approach with the intention of assisting others to produce impactful and socially robust knowledge, maximising the real-world impact of the findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".