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Record W4250238946 · doi:10.51166/ser/522humes

Re-Shaping the Policy Landscape in Scottish Education, 2016-20: The Limitations of Structural Reform

2020· article· en· W4250238946 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsAccountabilityPoliticsPublic administrationParliamentBureaucracyTransparency (behavior)SociologyDemocracyPower (physics)Public relationsProtectionismPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

This paper examines the establishment and operation of a number of new bodies – variously called councils, boards, collaboratives, groups, forums and panels – concerned with the development of Scottish education. What were the intentions behind their creation during the period 2016-20? Do they amount to a significant reshaping of the policy community, making it more open and democratic, and representing a genuine re-distribution of power, or are they more concerned with public presentation and political positioning? The paper is based mainly, but not exclusively, on publicly available minutes and related papers produced by the various bodies. These allow for an analysis of their composition and remits, as well as an examination of the substantive issues they have considered. The discussion also takes account of earlier descriptions of the character of the policy community, as well as hopes that the creation of the Scottish Parliament in 1999 would lead to greater transparency and accountability in political decision-making. It is argued that, while the new bodies provide opportunities for some previously marginalised voices to be heard, they demonstrate the continuing potency of familiar forms of bureaucratic management and professional protectionism. The paper also indicates a number of areas where further research would deepen understanding of the politics of Scottish education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.099
GPT teacher head0.350
Teacher spread0.251 · 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 designQualitative
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

Citations12
Published2020
Admission routes1
Has abstractyes

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