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Record W2087156796 · doi:10.1093/scipol/scu003

In discursive negotiation: Knowledge and the formation of Finnish innovation policy

2014· article· en· W2087156796 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

VenueScience and Public Policy · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsInstitute on Governance
FundersTekes
KeywordsNegotiationConvergence (economics)Process (computing)Policy analysisSocial constructivismPolicy learningSociologyEconomic systemPolitical sciencePositive economicsEconomicsSocial sciencePublic administrationEconomic growthComputer science

Abstract

fetched live from OpenAlex

This paper analyses the formation of Finnish innovation policy from the mid-1980s to 2010. Inspired by Foucauldian thinking in line with selected social-constructivist policy approaches, it conceptualises innovation policy as a discourse constituted of policy knowledge and policy-making practices. Our alternative approach towards policy formation, introduced in this paper, highlights the role of rules, and gradual changes in these, in defining truth values in policy knowledge, which in turn actualise in policy practice. The paper shows three phases in the investigated policy in Finland. Based on theoretical insights on policy formation, the paper argues that changes in innovation policy cannot be explained as a rational learning process or as isomorphic convergence processes across countries. Rather, they are an outcome of highly politicised negotiations in trans-local contexts where the role of a nation state can vary over time. Another finding is that changes in policy occur in relatively slowly.

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 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 score0.314
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.273
Teacher spread0.245 · 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