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Record W4382775516 · doi:10.1002/pan3.10496

Guiding principles for transdisciplinary sustainability research and practice

2023· article· en· W4382775516 on OpenAlexafffundabout
Maureen G. Reed, James P. Robson, Mariana Campos Rivera, Francisco Chapela, Iain J. Davidson‐Hunt, Peter Friedrichsen, Eleanor R. Haine, Anthony Blair Dreaver Johnston, Gabriela Lichtenstein, Laura S. Lynes, Majing Oloko, Michelle Sánchez Luja, Sheona Shackleton, Marlene Soriano, Fermín Sosa Pérez, Liette Vasseur

Bibliographic record

VenuePeople and Nature · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsCanmore Museum and Geoscience CentrePrince Albert Grand CouncilBrock UniversityUniversity of ManitobaEnvironment and Climate Change CanadaResearch ManitobaUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Technology SydneyBrock UniversityUniversity of Saskatchewan
KeywordsSustainabilityEngineering ethicsSociologyAccountabilityInclusion (mineral)Transformative learningPublic relationsGeneral partnershipHonourPolitical sciencePedagogySocial scienceEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract Transdisciplinary sustainability scientists are called to conduct research with community actors to understand and improve relations between people and nature. Yet, research hierarchies and power relations continue to favour western academic researchers who remain the gatekeepers of knowledge production and validation. To counter this imbalance, in 2018 we structured a multi‐day workshop to co‐design a set of principles to guide our own transdisciplinary, international and intercultural community of practice for biocultural diversity and sustainability. This community includes community collaborators, partner organizations, and early career and established researchers from Argentina, Bolivia, Canada, Germany, Mexico and South Africa. In 2021, we undertook online critical reflection workshops to share our research experiences and deepen our intercultural understanding of the application of the principles. Through these exercises, we adopted seven principles for working together that include: honour self‐determination and nationhood; commit to reciprocal relationships; co‐create the research agenda; approach research in a good way: embed relational accountability; generate meaningful benefits for communities; build in equity, diversity and inclusion; and emphasize critical reflection and shared learning. We explain these principles and briefly highlight their application to our research practices. By sharing these principles and associated practices, we seek to facilitate debate and spur transformations in how we conduct international and intercultural sustainability research. Our efforts also illustrate a strategy for on‐going knowledge co‐production as we cultivate safe and ethical spaces for learning together. Lessons learned may be particularly useful to those who engage in intercultural, collaborative research to advance sustainability transformations. Read the free Plain Language Summary for this article on the Journal blog.

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)0.231
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.231
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.114
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.004
Science and technology studies0.0150.075
Scholarly communication0.0290.014
Open science0.0080.021
Research integrity0.0160.021
Insufficient payload (model declined to judge)0.0060.004

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 head0.320
GPT teacher head0.589
Teacher spread0.270 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations40
Published2023
Admission routes3
Has abstractyes

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