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Record W6903374480 · doi:10.11575/prism/49503

Belongingness of Newcomers Through Community Engagements: A Racialized Woman’s Experiences

2023· other· en· W6903374480 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2023
Typeother
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBelongingnessEmpowermentMeaning (existential)AutoethnographySolidarityPresentation (obstetrics)Transformative learning

Abstract

fetched live from OpenAlex

This presentation tells the story of a racialized settler woman’s transformative journey toward belongingness and mutual empowerment through community engagements in Saskatoon, Canada. I will discuss how Indigenous Land-based learning became healing and empowering for me as a newly arrived settler woman of colour. I will also focus on how collaborative community learning has supported taking responsibility for understanding the meaning of Land in solidarity with Indigenous and newcomer communities through involvement in community garden activities, community radio shows, and various anti-racist, cross-cultural cultural community activities. Using decolonial, feminist, and relational autoethnography as my research methodology, this presentation will discuss my twelve years of community engagements in Treaty 6 territory. This presentation will also address how I challenged everyday racism and colonial practices ingrained in the daily lives of newcomer Canadians through community engagements. My doctoral research emphasizes a key lesson from this life journey: the need to be responsible for understanding the Indigenous meaning of Land to create belongingness with the Land and its original peoples while resisting the assimilationist forces affecting Indigenous and newcomer communities through their unique histories, despite the orchestrated biases operating through colonialist structures. The author concludes with the hope that analyzing decolonial, collaborative learning stories and connections with the Land and cross-cultural community engagements may help other non-Indigenous communities build meaningful relationships with the Land and create belongingness.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.319
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0670.028
Scholarly communication0.0100.005
Open science0.0030.016
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0040.001

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 head0.131
GPT teacher head0.427
Teacher spread0.296 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicIndigenous and Place-Based EducationFrench-language works237,207