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Record W2071144327 · doi:10.1556/aoecon.62.2012.4.2

Open Innovation in Portugal

2012· article· en· W2071144327 on OpenAlex
Aurora A.C. Teixeira, Mariana Mendes Lopes

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueActa Oeconomica · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierBusinessAbsorptive capacityTechnology gapIndustrial organizationOpen innovationQuarter (Canadian coin)Perspective (graphical)Technology developmentBridge (graph theory)Technological changeMarketingEconomicsInternational tradePolitical science

Abstract

fetched live from OpenAlex

The empirical studies in the area of Open Innovation (OI) reveal that there is a significant bias in favour of countries on the technological frontier. The present study aims to bridge this gap by examining firms in Portugal, a country at an intermediate stage of technological development. Based on 70 innovative firms, we found that whatever perspective of the OI model is considered, firms tend, on average, to share a relatively closed innovation model when compared with firms located in countries where technological development is advanced. About a quarter of the surveyed firms implemented the OI model in their innovation strategy/business, this being much more widely disseminated regarding the absorption of external knowledge/technology, with almost 40% of firms surveyed acknowledging its use in comparison with the perspective of transfer of knowledge/technology to other organisations — less than 10% provide their “surplus technology” to other organisations. This result may indicate a lack of awareness of the economic potential of making internally created technologies available to third parties, albeit this potential might also depend on other circumstances such as technology architecture (the system and interdependence of technologies).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.045
GPT teacher head0.270
Teacher spread0.225 · 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