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Record W2726378942

Making Space for Intersecting Worldviews in Teacher Education Programs in Canada, Namibia, and Colombia

2017· article· en· W2726378942 on OpenAlexaffabout
Christine Massing, Larry Prochner, Ailie Cleghorn, Anna Kirova

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsConcordia UniversityUniversity of AlbertaUniversity of Regina
Fundersnot available
KeywordsPedagogyIndigenousCurriculumSociologyGeneral partnershipFocus groupEthnographySocializationTeacher educationEarly childhood educationQualitative researchPolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

This presentation draws upon the findings in a study of early childhood teacher education (ECTE) programs in three social contexts: a workplace-embedded program for immigrant/refugee educators in Canada, a university-based ECTE program in Namibia, and a partnership between a Colombian Misak indigenous community and a local university to deliver community-based courses for educators. Framed by critical pedagogy, the study considered the extent to which teacher educators and their students adhered to aspects of the dominant Euro-American global view, as reflected in their country’s ECE policy and practice, or integrated that knowledge with indigenous views stemming from their home cultures. The study used ethnographic methods to undertake fieldwork in teacher education classrooms and early childhood programs at the three sites. Qualitative data were collected in the form of documents, field notes, semi-structured interviews, focus groups/meetings, emails, and informal conversations. The findings illumine various tensions between the local understandings and global ideas, such as understandings of early socialization, curriculum content, pedagogical approaches and tools, and language.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.995

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.138
GPT teacher head0.383
Teacher spread0.244 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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