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Record W3045594295 · doi:10.2478/jped-2020-0008

Canadian colonialism, ignorance and education. A study of graduating students at Queen’s University

2020· article· en· W3045594295 on OpenAlexafffundabout
Anne Godlewska, Laura Schaefli, Melissa Forcione, Christopher Lamb, Elizabeth Nelson, Breah Talan

Bibliographic record

VenueJournal of Pedagogy / Pedagogický casopis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsQueen's University
FundersQueen's University
KeywordsIndigenousColonialismMulticulturalismIgnoranceIdentity (music)ConstitutionPolitical scienceGender studiesSociologyEthnologySocial scienceEnvironmental ethicsLawEcology

Abstract

fetched live from OpenAlex

Abstract Canada has long been a colonial country and an extractive economy. In the 20th century, with the adoption of multiculturalism and a global peace keeping mission, the country seemed to embrace a new ethos. However, Canada remains deeply colonial and, in spite of a judiciary that since the repatriation of the Constitution in 1982, increasingly recognizes Indigenous land, resource and identity rights, its economy continues to be extractive, with abiding impacts on the Indigenous peoples of Turtle Island (North America). Our study of the knowledge, ignorance and social attitudes of exiting undergraduate students at Queen’s University suggests that students in this part of Canada (Ontario) are educated to misunderstand the fundamental geographies of Indigenous peoples, their land, and their identity. But the contradiction between image and reality is beginning to attract the students’ attention and disrupt their sense of being part of a just society.

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)0.007
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 score0.038
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0220.007
Scholarly communication0.0060.001
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.181
GPT teacher head0.519
Teacher spread0.339 · 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

Citations9
Published2020
Admission routes3
Has abstractyes

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