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Record W3047079625 · doi:10.25071/1916-4467.40516

Symposium: Emerging Perspectives on the Internationalization of Curriculum Studies

2020· article· en· W3047079625 on OpenAlexafffundvenue
Ashwani Kumar, Susan M. Brigham, Adrian M. Downey, Amélie Lemieux, Mohamed Kharbach, Bonnie Petersen, Sean Wiebe

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Prince Edward IslandUniversity of New BrunswickMount Saint Vincent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCurriculumPosthumanSociologyIdeologyInternationalizationIndigenousPoliticsCurriculum studiesCritical theoryCurriculum theoryColonialismConversationEnvironmental ethicsSocial scienceCurriculum developmentEpistemologyPedagogyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This symposium is concerned with understanding the forces that shape and influence curriculum in international contexts. The study of curriculum in international contexts reveals the insidious impacts of colonial, ideological and neoliberal influences on contemporary curriculum development in a variety of geo-cultural, political and economic contexts (Kumar, 2019). Four theoretical responses—Indigenous, critical, autobiographical and meditative—that provide thoughtful perspectives to challenge these negative influences will be explored in the symposium. The impact of intellectual movements such as Marxism and postmodernism on curriculum theory in varied political and economic settings will also be underscored. The symposium invites and initiates a complicated conversation around the internationalization of curriculum studies by inviting panelists from posthuman, Indigenous, black feminist, critical discursive and foundational perspectives to respond to the aforementioned colonial, ideological and neoliberal influences on curriculum development.

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)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.017
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 science0.0000.000
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 head0.044
GPT teacher head0.357
Teacher spread0.313 · 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
Published2020
Admission routes3
Has abstractyes

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