MétaCan
Menu
Back to cohort
Record W4376505232 · doi:10.1080/00220620.2023.2211911

Working with and against the bureaucratic state: histories of grassroots organising for public education reform, 1970s–1980s

2023· article· en· W4376505232 on OpenAlexaboutno aff
Helen Proctor, Jessica Gerrard, Susan Goodwin

Bibliographic record

VenueJournal of Educational Administration & History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsBureaucracyState (computer science)Context (archaeology)Public administrationPolitical scienceSociologyPublic relationsPoliticsLawGeography

Abstract

fetched live from OpenAlex

The article introduces an international Special Issue that addresses the significant question of how and why people organise to engage with policy making in the public sphere of education, from a historical perspective. Focusing on the phenomenon of ‘grassroots' community organising during the key formation period of the 1970s and 1980s, the issue collects articles from Australia, Canada, Chile, South Korea, Spain (Catalonia) and the United States for the purpose of examining what ‘grassroots' organising might look like or encompass under different kinds of states and state bureaucratic arrangements. This introductory article outlines the editors’ own research into the Australian context before highlighting how the various articles individually and collectively contribute to questions of 1) expanding understanding of what it means to organise at the grassroots level; 2) the complex relationships between state and school; 3) connections and disconnections between the local, national and international educational domains; and 4) how, methodologically, to capture people, experiences and organisations that may be fully or partly absent from official top down historical records.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.131
GPT teacher head0.362
Teacher spread0.231 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational Administration & HistorySame topicEducator Training and Historical PedagogyFrench-language works237,207