MétaCan
Menu
Back to cohort
Record W4402331911 · doi:10.29173/rssj11

‘Standing with Each Other’: Indigenous-Muslim Relation-Making on Turtle Island

2024· article· en· W4402331911 on OpenAlexaboutno aff
Memona Hossain

Bibliographic record

VenueReligious and Socio-Political Studies Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTurtle (robot)Relation (database)GeographyEthnologyFisheryHistoryEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

Within the context of the northern part of Turtle Island, the space of relationships between Indigenous and Muslim communities is one intertwined within the history and geopolitical realities of settler-colonialism and immigration. This paper is an exploration of the theme of space, and relationship formation from the perspective of Muslim and Indigenous peoples in Canada who have engaged in building relations over the past two decades. This article is based on a wider qualitative semi-structured interview-based research project, supported by content analyses of existing literature and online resources produced by relevant organizations and initiatives. The research analysis has led me to thematically organize these spaces into four general types of spaces: 1) organization-led spaces of relationship building; 2) spaces of conviviality as pathways to relationship building; 3) relational spaces defined through acts of documentation; and 4) spiritually and emotionally bonded spaces that transcend a secular framework. This analysis led to identifying practices of relational meaning-making that form a preliminary understanding of what characterizes Indigenous-Muslim relations on Turtle Island.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.495
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.023
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.350
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueReligious and Socio-Political Studies JournalSame topicCaribbean history, culture, and politicsFrench-language works237,207