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Through Parents’ Eyes: An Activist Visual Literacy Project

2010· article· en· W2460811753 on OpenAlexafffundabout
Sandra R. Schecter, Lorraine Otoide

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsYork University
FundersMinistère de l’Éducation, Gouvernement de l’Ontario
KeywordsVisual literacyLiteracyPsychologyOptometryVisual artsSociologyMathematics educationPedagogyArtMedicine

Abstract

fetched live from OpenAlex

This visual literacy study, embedded in an intergenerational instructional innovation at an urban primary-junior school in Ontario, Canada, used photography to ascertain caregivers' perceptions of their children's everyday experiences.Photographs, logs, and discussion sessions documented the culture-filtered understandings and sensibilities of caregivers as they reflected upon children's educational experiences in their newly adopted country.Beyond this anticipated function, these data sources served as conduits through which professional educators could access domains of knowledge related to spheres of influence in children's lives that are not normally discovered through standard schooling practices.Equally importantly, they helped immigrant parents to access, and better understand, the diverse resources that children call upon as they navigate various aspects of their Diaspora experiences.Thusly, the photographs functioned not simply as representational icons that substituted for verbal texts but also as heuristics that drove participants' thinking about the psychological and social worlds of immigrant students.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.521
Teacher spread0.478 · 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 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

Citations6
Published2010
Admission routes3
Has abstractyes

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