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Record W1594180956 · doi:10.2307/1602201

Through a Class Darkly: Visual Literacy in the Classroom

2001· article· en· W1594180956 on OpenAlexvenueno aff
Deborah L. Begoray

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)LiteracyVisual literacyMathematics educationPsychologyPedagogySociologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

“Viewing and Representing in the Middle Years” was a two-year project to investigate visual literacy in the English language arts classrooms of three teachers. These teachers tried a variety of approaches and were generally optimistic about the benefits of the increased inclusion of visual materials. They did, however, report a number of challenges in using viewing and representing approaches as part of their curriculum. Teachers’ previous experiences influenced their implementation of an expanded notion of literacy in English language arts, as did the influence of the university-based researcher conducting this investigation. L’article porte sur la visualisation et la représentation dans les premières années du secondaire dans les cours d’arts langagiers en anglais. Trois enseignants ont essayé diverses approches. Bien qu’ils voyaient d’un bon œil le fait d’inclure davantage de matériel visuel, ils ont signalé plusieurs difficultés reliées à l’utilisation d’approches de visualisation et de représentation dans leurs programmes. Les expériences antérieures des enseignants, tout comme la présente recherche, ont exercé une influence sur leur façon d’implanter une notion élargie de littératie dans les arts langagiers en anglais.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.293
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Education / Revue canadienne de l éducationSame topicLiteracy, Media, and EducationFrench-language works237,207