MétaCan
Menu
Back to cohort
Record W2953682176 · doi:10.1080/00933104.2019.1626783

Seeing and feeling difficult history: A case study of how Canadian students make sense of photographs of Indian Residential Schools

2019· article· en· W2953682176 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTheory & Research in Social Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInjusticeFeelingSet (abstract data type)Social injusticePsychologySocial studiesSocial psychologyPedagogySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Students in social studies classrooms are faced with a barrage of images, many of which represent historical trauma and violence. Although photographs can be used as pedagogical tools to represent experiences of injustice and elicit deeper understanding, they also activate affective and unrelated responses in students. In this case study, I explore the responses of Canadian secondary students to a set of historical photographs found in textbooks and resources that focus on the Indian Residential Schools. Findings from the study indicate that student responses to images representing difficult knowledge are unpredictable. Students were affectively and emotionally provoked by the photographs to both accept and deny abuse, as well as make personal connections to their own experiences of schooling. These findings raise questions around the best ways to use photographs of historical injustice in classrooms, as well as the ethics of using photographs that represent the suffering of others.

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.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.149
GPT teacher head0.469
Teacher spread0.320 · 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