Rethinking Knowledge Cumulation: Foregrounding Epistemic Justice in Environmental Governance Research
Bibliographic record
Abstract
ABSTRACT Social science inquiry into environmental governance is theoretically and methodologically diverse, resulting in a large array of isolated pieces of knowledge. Scholars' reflections around knowledge cumulation focus on how separate bits of knowledge can feasibly be integrated to build a broader, consensual state of knowledge. Yet, experience shows that transferring knowledge from existing research to a new case can lead to ill‐adapted governance solutions. We argue that this points to a disconnect between scholars' approaches to knowledge cumulation and cumulation efforts that create actionable knowledge. Indeed, we find there is little concrete guidance offered to scholars on which rationale should guide knowledge cumulation, limiting their capacity to effectively produce actionable knowledge. In this article, we suggest giving precedence to epistemic justice instead of strict feasibility in knowledge cumulation. As a first step, we review common blind spots in knowledge cumulation efforts and argue that a perspective grounded in epistemic justice is best suited to address (global) environmental issues. As a second step, and while acknowledging the structural and institutional limits within which scholars operate, we propose that they can contribute to a shift in the principles guiding knowledge cumulation. This transformation towards epistemic justice should be pursued already at various stages of the knowledge production process, namely in conducting research, presenting and publishing research, and communicating research to policy‐makers and communities. This article is primarily directed at environmental governance scholars in the social sciences but may offer valuable insights for anyone interested in inter/trans‐disciplinary and boundary‐spanning approaches to science and policy‐making.
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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 | 0.233 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.019 | 0.225 |
| Scholarly communication | 0.037 | 0.061 |
| Open science | 0.006 | 0.049 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".