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Record W4311741839 · doi:10.1080/09654313.2022.2156273

Knowledge sourcing by multi-plant firms in Europe

2022· article· en· W4311741839 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

VenueEuropean Planning Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsBusinessKnowledge productionProduction (economics)Industrial organizationKnowledge managementValue (mathematics)MarketingEconomic geographyEconomicsComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Research on geographies of knowledge sourcing examines the organizational structure of innovation activities within the firm, the mechanisms by which knowledge is extracted from various external sources and the geography of these different activities. We augment this literature by exploring knowledge sourcing within multi-plant firms operating in Europe. Analysis makes use of linked patent-firm data recording the location of knowledge production and its ownership. The results add value to existing research in three ways. First, the establishments of multi-plant firms are shown to produce different kinds of knowledge in different locations. Second, the patents generated within a firm's establishments are linked to the knowledge stocks of the regions where they operate, supporting a vision of geographical knowledge sourcing. Third, the complexity of knowledge produced within firms is positively related to the number of plants in which they innovate.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.071
GPT teacher head0.288
Teacher spread0.217 · 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