From interdisciplinary to inter‐epistemological approaches: Confronting the challenges of integrated climate change research
Bibliographic record
Abstract
Spurred by the literature on climate change and its calls for undertaking holistic research that more fully integrates the work of biophysical and social scientists, this article responds to the question: To what extent has climate change research in Canada embraced and been guided by the theories and tenets associated with interdisciplinarity and to what extent have integrated approaches been sensitive to cross‐cultural perspectives? It provides an overview of some of the epistemological issues raised in the interdisciplinarity literature that particularly impact research development and design. Furthermore, since much of the climate change literature that claims to be integrated or interdisciplinary draws from Indigenous Knowledge (IK), additional insights are provided from this perspective. The article develops a framework that can be used to undertake and/or evaluate research in a way that acknowledges “upstream” epistemological issues. The framework is then used to evaluate a comprehensive database (n = 282) of Canadian climate change articles. It is argued that an interdisciplinary approach adds a critical voice to the literature on integrated climate change research and is valuable because of its focus on epistemology and methodology. The article advocates the creation of a space for inter‐epistemological acknowledgement in which the academy develops an ethos of self‐reflection, while simultaneously respecting and integrating parallel knowledge frameworks, such as IK.
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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 | 0.123 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.028 | 0.094 |
| Scholarly communication | 0.042 | 0.027 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.005 | 0.009 |
| 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".