Silent violence in the just transition: structural barriers, governance design, and the hidden costs of climate policy
Bibliographic record
Abstract
The concept of silent violence refers to the hidden harm embedded in policy and economic systems, manifesting as the repression of activists, displacement of communities, and exploitation of labour across transitions to low-carbon economies. This article examines how structural barriers embedded in global just transition policies and energy governance frameworks produce forms of silent violence (SV) that disproportionately harm marginalized communities. Drawing on a comparative, multi-case analysis from Bolivia, Canada, South Africa, and Brazil, the study argues that SV is not accidental but a governance-enabled outcome, manifested through policy loopholes, non-consultative permitting, regulatory capture, and enforcement failures. Conceptually, SV is framed as a subset of structural violence that remains legally unframed, institutionally normalized, and largely invisible in climate policy discourse. The article advances a typology of silent violence, ranging from soft forms (epistemic exclusion, procedural marginalization) to hard forms (criminalization, state repression, and lethal harm). We introduce the Silent Violence Continuum as an analytical tool to map how different governance instruments condition escalating harms under the guise of sustainable development. The study contributes to critical climate justice scholarship by showing how SV operates as a design feature of transition governance rather than a failure. The article calls for the integration of silent violence metrics into climate policy evaluation to support more equitable, transparent, and non-violent transitions.
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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".