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Record W7064433694

The art of uncomfortable engagement: towards a methodology for conflictual theatre dialogues about climate change controversies

2024· article· en· W7064433694 on OpenAlexaff

Bibliographic record

VenueVU Research Portal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsNucleofectionArticular cartilage damageDemotionCircumstantial evidenceGestational periodTSG101
DOInot available

Abstract

fetched live from OpenAlex

Climate change is increasingly proving to be a deeply polarizing and conflict-ridden societal issue. As a response to complex or “wicked” societal issues such as climate change, science communication research has developed various approaches to meaningfully engage publics and stakeholders in processes of scientific and technological development. Building on Chantal Mouffe’s conception of agonistic pluralism, we argue that while providing a great source of creativity, inspiration and impact, the “cocreative” focus of many of these approaches makes it difficult to keep conflict, tension, and controversy at the forefront of engagement practices. The concern is that these practices of cocreation leave the fundamentally conflictual nature of climate change unaddressed, and consequently provide little guidance for learning how to deal with conflict and prevent it from becoming antagonistic.<br/><br/>There is therefore a great need for exploring engagement approaches that make the conflictual nature of climate change salient and “productive”, and we believe theatre dialogues have great potential to contribute to this objective. In our contribution, we report on the outcomes of interviews held to map three climate change-related controversies and stakeholders’ experiences of conflict within them, and on how these key findings inform the development of an innovative form of theatre dialogues in which sticking with the tension is key.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.000

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.

Opus teacher head0.231
GPT teacher head0.431
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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