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Record W2743527322 · doi:10.22215/sjcs.v7i0.1169

Teaching Black Canada(s) Across Borders: Insights from the Caribbean and United States

2016· article· en· W2743527322 on OpenAlexaboutno aff
Amoaba Gooden, Charmaine Crawford

Bibliographic record

VenueSouthern Journal of Canadian Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaGender studiesCurriculumHegemonyNationalismSociologyNarrativePrivilege (computing)Diversity (politics)Human sexualityHistoriographyPolitical sciencePedagogyPoliticsAnthropologyLaw

Abstract

fetched live from OpenAlex

This article offers two unique and overlapping perspectives that explore the challenges and practices faced when teaching and researching Canada from the Caribbean and the United States. Acknowledging that curriculum, which ontologically and epistemologically influences the norms and values of the learning environment, is not a neutral document (Ross 2001, 1) the authors focus on how they enact curricula outside of Canada to discuss issues of rights and diversity as well as to challenge hegemonic narratives of white Canadian nation-building and historiography and ideas of Canada as a benevolent nation-state. Specifically, the authors examine (1) the ways in which assumptions about power and privilege play out in the classroom by either reinforcing or challenging established North/South relations; (2) how the black Canadian experience can be used to disrupt the U.S. black nationalist discourse and allow for an elaboration of the black or African Diaspora; (3) how dominant notions of gender, race and sexuality are articulated differently outside a Canadian landscape; and (4) Canadian-Caribbean relations when it comes to teaching and learning across borders through student exchange programmes.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

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 studies0.0020.001
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 head0.020
GPT teacher head0.261
Teacher spread0.242 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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