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Record W4414251389 · doi:10.1007/978-981-96-9029-9_2

Deliberative Futures Workshops for Transformative Sustainability

2025· book-chapter· en· W4414251389 on OpenAlexaff
Alexandra Revez, Clodagh Harris, Niall Dunphy, Brian Ó Gallachóir, Edmond P. Byrne, Evan Boyle, Connor McGookin, John Barry, Paul Bolger, Fionn Rogan, Geraint Ellis, Barry O’Dwyer, Stephen Flood, Elizabeth Creed, Gerard Mullally

Bibliographic record

VenueScience for sustainable societies · 2025
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransformative learningFutures contractDeliberative democracySustainabilityProcess (computing)DeliberationAction (physics)TransdisciplinarityDiversity (politics)

Abstract

fetched live from OpenAlex

Abstract The deliberative futures workshop is both a method and a model of engagement that is designed to support transformative change, thinking ahead, and getting communities involved in shaping their future. It promotes collaboration with experts, stakeholders, and communities in a co-productive process that aligns with deliberative action research. The process seeks to be inclusive of everyone with a stake in the future by facilitating the development of multiple tools for their involvement in the planning and decision-making processes, following the deliberative principles of equality, inclusion, and reasoned argument. The deliberative futures workshop embraces a diversity of communication and practices that recognize the systemic turn in deliberative democratic theory. The various techniques proposed draw upon multiple experiences, voices, and perspectives with a view to increasing and enhancing the number of “deliberative” moments within the process.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0060.007
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0580.007

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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