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Record W4316094395 · doi:10.1111/caim.12536

Which types of firm use collaborative innovative spaces?

2023· article· en· W4316094395 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCreativity and Innovation Management · 2023
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsMcGill UniversityHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaAgence Nationale de la Recherche
KeywordsContext (archaeology)BusinessCreativityOrder (exchange)Knowledge managementIndustrial organizationOpen innovationSpace (punctuation)MarketingEconomic geographyEconomicsComputer sciencePsychologyGeography

Abstract

fetched live from OpenAlex

Collaborative innovation spaces (CIS) can bring together multiple actors to enhance creativity, collaboration and knowledge exchange, sometimes leading to innovation. In this paper, we suggest that CIS can be categorized into three broad types (internal to the firm, external and virtual) and that each type is related to innovation processes, knowledge‐sourcing and geographic context in specific ways. Our results, based on an original firm‐level survey, reveal that there is heterogeneity with respect to firm attributes and innovation activities associated with different types of CIS. In particular, whilst innovation is associated with the use of CIS in general, radical and technological innovations are more strongly associated with internal CIS, whereas smaller firms tend to use virtual CIS. External CIS, whilst not associated with technological innovation, are associated with high‐tech firms. CIS use does not vary across geographic context. These results emphasize the importance of in‐person, internal, CIS for radical and technological innovation and the need to distinguish between different types of CIS in order to understand how each is associated with different types of innovation, knowledge‐sourcing and firm.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.010
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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