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VOICES ACROSS BORDERS: EXPLORING THE INDO-CANADIAN DIASPORA AND INDIGENOUS LITERATURES IN CANADA

2024· article· en· W4405085763 on OpenAlexaboutno aff
Raja Sekhar, Narasimha Raju

Bibliographic record

VenueShodhKosh Journal of Visual and Performing Arts · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaIndigenousIndo-PacificPolitical scienceGeographyHistoryEthnologyAnthropologyGender studiesSociology

Abstract

fetched live from OpenAlex

This paper delves into the rich literary traditions of Canada’s Indigenous peoples and the narratives of the Indo-Canadian diaspora, highlighting their contributions to the Canadian multicultural tapestry. Indigenous oral and written literature, though diverse and profound, remains marginalized within traditional literary studies. Similarly, the Indo-Canadian diaspora offers compelling stories of migration, resilience, and cultural integration, reflecting the enduring ties between India and Canada. From the challenges faced by early Punjabi migrants to the vibrant contributions of subsequent generations, the journey of Indo-Canadians underscores the complexities of dual identity, cultural hybridity, and systemic inequities. Indo-Canadian literature serves as a lens into these experiences, exploring themes of displacement, cultural negotiation, and identity conflicts. Works by authors such as Rohinton Mistry, Anita Rau Badami, and Shauna Singh Baldwin bring to life the struggles and triumphs of the Indian diaspora, while connecting them to broader discourses on multiculturalism and belonging. The narratives of Indigenous and Indo-Canadian authors challenge societal norms, celebrate diversity, and interrogate systemic inequities, offering critical insights into Canada’s literary and cultural evolution. This paper underscores the need for deeper scholarly engagement with these literatures to appreciate their complexity and transformative potential in redefining multicultural identity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.023
GPT teacher head0.304
Teacher spread0.280 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueShodhKosh Journal of Visual and Performing ArtsSame topicCanadian Identity and HistoryFrench-language works237,207