Practicing Climate Action: Following Climate Change Education Practice Elements in a K-12 School Using a Whole Institution Approach
Bibliographic record
Abstract
As humans, we now possess more knowledge about actions needed for planetary rehabilitation than hitherto seen before; however, current climate actions remain insufficient to address the most deleterious effects of climate change. With fewer than twelve years remaining to prevent climate catastrophe, it is imperative to recognize knowledge as more than cognitive accumulation. Most climate change education and research to date, however, has focused on instilling individual scientific cognitive clarity instead of learning how to do climate actions together. This study utilized a practice lens to adjust conceptual focus away from the knowledge of individual learners to the climate action practices they collectively ‘carry,’ (un)equally share, and mutually shape, wherein understandings, meanings, and purposes are irreducible to personal attributes. Shove and colleagues’ conceptualization of practices was used to examine climate action practices occurring at a Kindergarten to Grade 12 school in Canada using a whole institution approach to climate change education. A whole institution approach includes climate change education within and/or across each of the domains of Overall Governance, Teaching and Learning, Community Partnerships, and Facilities and Operations The data generation methods used in the study included a sensory walk, observations, interviews, focus groups, document collection, and photography. The findings illustrate how the climate action practices observed and described by participants emerged, endured, and disappeared through a complex set of interactions, influences, (dis-)(re-)connections, motivations, and forms of monitoring. Key climate action practice elements (i.e., materials, competences, and meanings) within each of the four domains were followed and are described, as well as significant connections within and across whole institution domains. This research has implications for climate change education practice, policy, and research, which include 1) the potential of using a whole institution approach to climate action in education, 2) how to support the emergence, endurance, and disappearance (if needed) of practice elements, as well as connections between practice elements, and 3) how practice theory is beneficial for CCE research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".