In discursive negotiation: Knowledge and the formation of Finnish innovation policy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".