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Record W2762945084

We Cannot Call Back Colonial Stories: Storytelling and Critical Land Literacy

2017· article· en· W2762945084 on OpenAlex
rosalind hampton, Ashley DeMartini

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsStorytellingIndigenousLiteracyNarrativeSociologyCitizen journalismColonialismRelation (database)Critical literacyDigital storytellingPedagogySocial scienceMedia studiesPolitical scienceGeographyEcologyArchaeologyLiteratureLawArt
DOInot available

Abstract

fetched live from OpenAlex

This article examines the role of stories and storytelling in both shaping and revealing pre-service teachers’ understandings of land. The authors conducted a study using digital storytelling as a participatory method of inquiry examining participants’ conceptions of land. Participants’ narratives reflect stories they have been told about their families, communities, and nations, revealing inextricable links between conceptions of land, nation, and self in relation to others. The authors propose the notion of critical land literacy as a pedagogical goal in Teacher Education. They define critical land literacy as an understanding of, and relation to, land informed by Indigenous knowledges and a critique of ongoing settler-colonialism in Canada.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.072
GPT teacher head0.304
Teacher spread0.232 · 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