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Record W4283741139 · doi:10.1007/s13280-022-01751-x

Getting personal with collaborative sustainability experimentation: Reflections and recommendations from a transdisciplinary partnership with the Swedish craft beer sector

2022· article· en· W4283741139 on OpenAlexfundno aff
Barry Ness, Darin Wahl

Bibliographic record

VenueAMBIO · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEnergimyndighetenLunds UniversitetJoint Programming Initiative Urban Europe
KeywordsCraftCoproductionGeneral partnershipSustainabilityReflexivityVariety (cybernetics)Knowledge managementSociologyLiving labEngineering ethicsPublic relationsBusinessEngineeringPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper provides reflections on transdisciplinary knowledge coproduction and experimentation processes from sustainability researcher perspectives. It centers on a 5-year period of collaborative research with small- and medium-sized enterprises in an Urban Living Lab in the Swedish craft beer sector. Nine reflections cover a variety of issues and potentials encountered during numerous interactions with societal partners, and are structured by three levels: organizational, interpersonal and intrapersonal. Based on the reflections, authors then propose a set of seven considerations and recommendations for how to more effectively collaborate in such transdisciplinary constellations. The recommendations apply across the three levels, and describe an approach to collaborative research that asks the researcher to be open, transparent, self-aware and intentional, reflective and reflexive, and both adaptive and flexible. Furthermore, they aim to create soft structures to facilitate understanding and mutual learning, such as designating "organizational champions", as well as to embed collaborative reflections into recurring meetings with partners to maintain trust and capture sustainability knock-on opportunities as they arise.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designQualitative
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

Citations7
Published2022
Admission routes1
Has abstractyes

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