Building a Coalition with Depoliticized Sustainability Discourse: The Case of a Transdisciplinary Transition Management Arena in Peru
Bibliographic record
Abstract
Transition management uses the depoliticized, rational discourse of systems terms, social learning and societal reflexivity. Transdisciplinary sustainability science research similarly uses the politically neutral terms of supporting the coproduction and integration of different types of knowledge. Yet both are clearly normative, resting on notions of participatory democracy and adopting environmental and social sustainability as explicit norms. Here we present the case of a transdisciplinary transition management arena in Peru, convened to develop a vision of a lower carbon, more decentralized and resilient national energy system. We show how the characteristics of the arena can help to foster the necessary conditions for empowerment and how these in turn both support – and are supported by - the ability of participants from different backgrounds generate shared problem statements, visions and strategies, building towards a coalition for change. While it remains to be seen how politically influential such arenas can be in the medium and long term, we show that depoliticized, rational sustainability discourse nonetheless has a political role to play in helping to legitimize informal institutional efforts towards energy policy change. Highlights Transition management and transdisciplinary sustainability science rationales are complementary These rationales are integrated in a Transdisciplinary Transition Management Arena (TTMA) framework The TTMA is applied with Peruvian energy system stakeholders holding marginalised views on energy futures A vision for a low carbon Peru is developed that emphasises the inclusion of distributed renewables The TTMA is assessed in terms of its capacity to support conditions for the empowerment of marginalised policy stakeholders
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".