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Collective Innovation for Complex Challenges

2021· book-chapter· en· W3212014123 on OpenAlex
Goran Matic, Ana Matić

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

VenuePractice, progress, and proficiency in sustainability · 2021
Typebook-chapter
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsPraxisCollective actionKnowledge managementField (mathematics)Engineering ethicsSociologyCollective intelligenceManagement sciencePolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Is has now become widely recognized that our world has become increasingly complexified and immersed in societal issues that require a diversity of perspectives to effectively engage. Collective innovation holds the promise of enabling a plurality of views necessary for creating effective innovation strategies. Yet collective processes are beset by a range of issues that are challenging for scholars, researchers, and practitioners to understand and effectively manage. Building on the complexity typologies theory as augmented by insights from the field of systemic design, the authors propose a missing element to enable collective action initiatives – identified as meta-cognitive skills critical to group collaboration and collective innovation processes. They illustrate the proposal with well-known examples and some of the latest studies in the field. They conclude by proposing next steps that educators or practitioners might employ in their own educational, curriculum design, and practice contexts – recognizing the key elements of praxis that connects them all.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.339
Teacher spread0.279 · 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