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Record W2911633003 · doi:10.1093/icc/dty059

The export additionality of innovation policy

2018· article· en· W2911633003 on OpenAlex
Mark Freel, Rebecca Liu, Christian Rammer

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

VenueIndustrial and Corporate Change · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsAdditionalityScope (computer science)Psychological interventionGermanBusinessEmpirical evidenceEconomicsIndustrial organizationPanel dataPublic economics

Abstract

fetched live from OpenAlex

The empirical evidence that innovation policies often lead to innovation additionality is long-standing. However, innovation is an intermediate outcome. Innovations are important to the extent that they contribute to some broader goal, such as the competitiveness of firms and economies. To this end, we take exporting as an important indicator of competitiveness and investigate whether innovation interventions lead to exporting outcomes. Using the Mannheim Innovation Panel, the current study explores whether innovation interventions at various administrative levels associate with changing export behaviors among German Small- and Medium-sized Enterprise (SMEs). Our results provide evidence of the scope for policymakers to employ innovation interventions as export policy.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.218

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 communication0.0000.000
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 head0.442
GPT teacher head0.293
Teacher spread0.150 · 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