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Record W3114530187

An exploration of anti-oppressive education in the high school ELA classroom

2019· dissertation· en· W3114530187 on OpenAlexaboutno aff
Kelly Fewer

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPedagogySociologyPolitical sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

In the past three years, the province of Manitoba has been implementing a new curriculum in English Language Arts (ELA), starting at the elementary level and more recently voluntary implementation at the high school level. An important change to the curriculum is the addition of the practice of power and agency, which can be viewed as an attempt by this curriculum to be anti-oppressive. Anti-oppressive education is the practice of teaching to all students by embracing their diversity, and creating safe spaces to actively work against various forms of social oppression (Kumashiro, 2000). The goal of this qualitative research study, that garnered data through interviews, is to discover how some high school ELA teachers in the province who self-identify as taking an anti-oppressive stance do so in their approach to students, curriculum and materials, and pedagogy. The findings show that these educators’ motivations for taking such a stance are grounded in their experiences teaching, their identities, and their professional learning. While their objectives for their teaching focused on the selection of resources, building relationships, and having meaningful class discussions. The implications of the study could guide teachers in their selection of resources, teaching and assessment tools, and pedagogical decisions in high school ELA.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.572
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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