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Record W7096004591

TO CITE THIS ARTICLE PLEASE INCLUDE ALL OF THE FOLLOWING DETAILS: Prud’homme, Marc-Alexandre. (2012). Reading Education’s Front Covers and Margins. Transnational Curriculum Inquiry 9

2016· article· en· W7096004591 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Context (archaeology)CurriculumPublishingFront (military)Order (exchange)
DOInot available

Abstract

fetched live from OpenAlex

There is an old saying that says that an image is worth a thousand words. I remember that, when I was young, to choose what book to buy or what movie to rent, the majority of my decisions rested on the front covers that appealed to me. Needless to say, I have been disappointed on more than one occasion with some of these decisions. Publishing companies and film producers, aware of this phenomenon, often hire teams of graphic designers to showcase their products. Most ministries of education are no exception to this. In the context of high-stake competitions between schools and between school boards, many ministries of education across the world are posting images as focal points on their websites to express various messages about their activities and about that of their schools. They design these images for parents, students and other stakeholders in education such as teachers, administrators, researchers, but also for members of the community. These images provide a glance into the world of school systems. They represent pieces of information to ponder in order to take informed decisions about education. With this in mind and Freire’s (1970) idea of reading one’s wor(l)d, in this paper, I will deconstruct and reconstruct images used by three Western ministries of education, those of Ontario, Oklahoma and France. Having attended a school in Oklahoma, studying in Ontario and being a francophone, I chose these ministries so that my familiarity with these regions

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.023
GPT teacher head0.259
Teacher spread0.235 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same topicArt Education and DevelopmentFrench-language works237,207