Rethinking Knowledge Cumulation: Foregrounding Epistemic Justice in Environmental Governance Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it