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
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.409 | 0.159 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".