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Record W1993033788 · doi:10.1177/0021886301374004

The Paradox of Multistakeholder Collaborative Roundtables

2001· article· en· W1993033788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2001
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsScale (ratio)EpistemologyCollaborative learningComputer sciencePsychologyPolitical scienceSociologyKnowledge managementPhilosophyGeographyCartography

Abstract

fetched live from OpenAlex

This study examines the outcomes of a large-scale Multistakeholder Collaborative Roundtable (MCR) on environmental protection. The findings shed a considerably more realistic light on the concrete outcomes of MCRs than does the image portrayed by the literature and some practitioners. We observed that consensus was achieved, albeit on general principles only. Various types of learning did occur, but they were limited to networking competencies. Problem solving was detected, albeit in the form of incremental innovation only. Overall, the major result of the MCR studied was that it contributed “small wins” to its initial grand objective. The case illustrates the paradox of MCRs. It teaches us that we should be cautious about their real potential to help solve complex collective problems. Yet, it shows that MCRs do serve a useful purpose, that of giving direction to “metaproblems, ” a result that apparently can hardly be attained otherwise.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.000
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 head0.032
GPT teacher head0.304
Teacher spread0.272 · 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