Collaborative Inquiry into Climate Change Information Literacy: A Participatory Approach with Young Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores how participatory research and mentorship enhance critical information literacy (CIL) regarding climate change information.Grounded in socioconstructivist theory, the research involved a Grade 11 student mentor working with Grade 6 students.Findings indicate that through collaborative activities, students developed their knowledge about climate change-related topics and strategies for critically evaluating digital information, demonstrating increased empowerment and engagement. Major Issues addressedDeveloping critical information literacy among the younger generation-empowering them to seek, evaluate, utilize, and create digital information for informed personal and collective decision-making-is paramount in contemporary education (Osborne et al., 2022; UNESCO, 2023).Young people increasingly use social media as a source of science-related information, including topics on climate change (Greenhow et al., 2015;Kresin et al., 2023).While these platforms can inform students, they also expose them to entertaining, commercial, and pseudoscientific content (Allchin, 2023;Dolan et al., 2019;Httecke & Allchin, 2020;Mavrodieva et al., 2019).Given this mixed landscape of information, science and climate change education needs to adapt to equip students with effective strategies to evaluate the credibility of climate-related information on social media for informed decision-making (Breakstone et al., 2018;Kresin et al., 2023;Osborne & Pimentel, 2023).This research aims to address the gap in understanding how youth perceive and interact with climate change information and seeks to enhance their critical information literacy (CIL) skills.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it