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Record W3171536445 · doi:10.22158/elsr.v2n2p50

Indigenous Knowledge, Indigenous Experiences with Residential Schools and Sixties’ Scoop, and their Impact on Emotional Knowledge for Pre-service Teachers

2021· article· en· W3171536445 on OpenAlexaffabout
Julia Falla-Wood

Bibliographic record

VenueEducation Language and Sociology Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsBurman University
Fundersnot available
KeywordsIndigenousBachelorPsychologyService (business)Exploratory researchPerceptionActive listeningMedical educationPedagogySociologyPolitical scienceSocial scienceMedicine

Abstract

fetched live from OpenAlex

The purpose of this 2019-2020 exploratory study is to examine pre-service teachers’ knowledge and perceptions of Indigenous Peoples and how emotional knowledge could efficiently integrate this sensitive aspect of Canadian history into the B.Ed. Program. Shen et al. (2009) state that emotions improve learning and facilitate retention in long-term memory. Could emotional knowledge be a way of integrating Indigenous knowledge in the Bachelor of Education programs? Could Indigenous experiences with Indigenous Peoples make a difference in the perception of Indigenous Peoples in pre-service teachers? For this study, the sample available to the researcher consisted of 22 pre-service teacher students. The research instruments were a questionnaire about pre-service teachers’ knowledge of Blanket Exercises, Residential Schools, and Sixties’ Scoop, and reflection papers on the same topics. The results show that 72% of Canadian pre-service teachers, who attended elementary and secondary schools, had some, very little or no knowledge of these topics before the former Prime Minister, Stephen Harper, apologized to former students of Residential Schools for the harm inflicted to them. After listening to Indigenous Survivors and being part of Blanket Exercises, pre-service teachers’ perception of Indigenous Peoples changed in a range of 26% to 100%.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.098
GPT teacher head0.491
Teacher spread0.393 · 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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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