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Record W3208904746 · doi:10.32920/ryerson.14646615.v1

Canada’s Welcomers Building Relationships Between Newcomer Settlement Organizations and Indigenous Organizations and Peoples

2021· preprint· en· W3208904746 on OpenAlexaffabout
Rachel Reesor

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndigenousSettlement (finance)MulticulturalismMainstreamColonialismGovernment (linguistics)Identity (music)Political scienceSociologyLawBusinessEcology

Abstract

fetched live from OpenAlex

Multiculturalism, part of Canada’s national identity, continues to integrate newcomers into mainstream society, which excludes Indigenous people. If Indigenous people became the welcomers of Canada by replacing multiculturalism with Treaties, they would be placed at the forefront of Canada’s national identity and acknowledged for being the First People of Canada rather than ignored. The purpose of my paper is to argue that settlement services should have to build relationships with Indigenous organizations and people in order to build relationships between Indigenous people and newcomers, that would allow newcomers to be better informed about Canada’s history and Canada’s Indigenous people. Within my research I will be critically analyzing six settlement organizations that have begun the process of building these relationships and present the benefits and challenges to building these relationships as well as what their next steps are for moving forward with this relationship. My findings will help to present recommendations for the government of Canada to assist in building these relationships. Keywords: Indigenous, newcomer, settlement services, decolonization, Indigenous resurgence, multiculturalism, colonialism, settler colonialism

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0360.011
Scholarly communication0.0090.003
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

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