MétaCan
Menu
Back to cohort
Record W2895079943 · doi:10.1787/235c9806-en

How is research policy across the OECD organised?

2018· report· en· W2895079943 on OpenAlexfundno aff
Martin Borowiecki, Caroline Paunov

Bibliographic record

VenueOECD science, technology and industry policy papers · 2018
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
FundersNational Plan for Science, Technology and InnovationNational Health and Medical Research CouncilAustralian Research CouncilMedical Research CouncilSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCentro para el Desarrollo Tecnológico IndustrialCanadian Institutes of Health ResearchÖsterreichische ForschungsförderungsgesellschaftVlaamse regeringAgencia Estatal de InvestigaciónFundação para a Ciência e a TecnologiaCouncil for Science and Technology PolicyFonds Wetenschappelijk OnderzoekWaalse GewestInstituto de Salud Carlos IIIAustrian Science FundChristian Doppler ForschungsgesellschaftHigher Education AuthorityInnovationsfonden
KeywordsPolitical scienceRegional scienceGeography

Abstract

fetched live from OpenAlex

Building on a newly created policy indicator database, this paper provides a first systematic comparison of the governance of public research policy across 35 OECD countries from 2005 to 2017. The database was obtained following a three-year process that involved the development of an ontology of the governance of public research policy as well as data collection and validation by national authorities. The data show diverse institutions and mechanisms of policy action regarding higher education institutions (HEIs) and public research institutes (PRIs) are in place across the 35 OECD countries. The data also shows an increasing use of project funding, performance contracts and performance evaluations for HEIs and PRIs. In many countries, HEIs and PRIs are autonomous regarding their relations with industry, budget allocation but less frequently regarding salaries. Recent reforms have strengthened external stakeholders' participation in their governance. The database is publicly available on the following webpage: https://stip.oecd.org/resgov.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.045
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.955
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.086
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0190.041
Science and technology studies0.0030.005
Scholarly communication0.0250.010
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.129
GPT teacher head0.390
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreOther

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".

Quick stats

Citations27
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueOECD science, technology and industry policy papersSame topicInnovation Policy and R&DFrench-language works237,207