Analysing responses to climate change through the lens of reflexivity
Bibliographic record
Abstract
Sociologists are increasingly directing attention toward social responses to climate change. As is true of any new field of inquiry, theoretical frameworks guiding the research to date have room for improvement. One advance could be achieved through closer engagement with Reflexivity Theory, particularly the work of Margaret Archer, who asks just how individuals come to give attention to certain problems, and formulate responses to them. Individuals vary significantly in regard to their understanding of and concern for anthropogenic climate change, and these standpoints in turn influence commitment to mitigation and adaptation. The emergent social interactions among all such agents in turn influence the morphogenetic trajectories through which social structures will evolve, but the role of 'meta-reflexives' is particularly crucial. Identifying pathways of individual climate change reflexivity can make a valuable contribution to our understanding of the potential for and nature of collective responses. In this paper, I explore climate change reflexivity, with particular attention to climate change meta-reflexives, through a qualitative analysis of personal interviews with residents of two small communities in Alberta, Canada. Applying Reflexivity Theory to this context articulates dimensions of reflexive processing not elaborated in current theoretical treatments, including future outlook and comfort with uncertainty, among others.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.113 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".