Bibliographic record
Abstract
Climate change is increasing the risk of hazards like flooding, drought, and wildfire for everyone, everywhere. But climate hazards do not affect us all equally. The lived experience of climate change is highly differentiated by social factors like gender, socioeconomic class, location, and culture. Both climate change and social inequality are real, structural, and perseverant problems, which intersect to shape people’s experience of a hazard. Positivist and interpretivist research approaches, which focus on the empirical level of reality, may not alone capture the magnitude and complexity of climate impacts or their causes. While positivism identifies important observable biophysical or economic impacts, it may reduce reality to these observable aspects. Relativism encourages consideration of multiple interpretations and perspectives; however, it may risk (re)opening the door to debates on the veracity of climate change or inequity. Critical Realism (CR) offers an alternative. Drawing on examples from the climate change literature and original results from a qualitative study with rural and Indigenous communities in Canada, I demonstrate the methodological utility of CR for analysing the impacts of climate hazards like drought, wildfire, and intense flooding, which are being worsened by climate change. Through its realist ontology and interpretivist epistemology, CR facilitates the identification and critique of causal structures that create and perpetuate climate injustice.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.058 | 0.014 |
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; both teacher heads agree on what is shown here.
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".