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Record W6981323288

Education Councils: Critical boundary actors bridging the worlds of policy, science, practice and society.

2021· article· en· W6981323288 on OpenAlexaboutno aff

Bibliographic record

VenueLirias (KU Leuven) · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Bridging (networking)Transparency (behavior)Education policyPublic policyFunction (biology)Bridge (graph theory)Civil society
DOInot available

Abstract

fetched live from OpenAlex

The European Network of Education Councils EUNEC celebrates its 20 years of existence with the publication of 10 essays that capture the variety and commonalities of education councils in Europe, Québec and Morocco. The contributions in this celebratory issue demonstrate how education councils produce advice, how they interact with stakeholders, and how they create transparency in the policy process. These education councils are clear examples of institutionalized advisory bodies that aim to strengthen the policy analytical capacity of governments. At the same time, education councils are institutionalized bodies for policy advice which ensure public participation and empower civil society actors. The policy advice that the councils bring to the table is in many cases based on consultation and participation of a variety of stakeholders within the educational field. In this way, education councils function as critical boundary actors that bridge the worlds of policy, science, practice and society. Many education councils have even managed to establish and maintain themselves as relevant and influential actors in the field of education. This is certainly not self-evident in a competitive and dynamic policy advisory system. In what follows, we will reflect in more detail on the comparative context, role, positioning and functioning of education councils and we will ground the discussion in recent insights on the policy advisory system and the nature and sources of policy advice to governments.

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 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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.324
Teacher spread0.313 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

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