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Record W7101898113 · doi:10.6084/m9.figshare.30471926

Are Canadian Indigenous Peoples Facing Marginalisation and Socio-Economic Disparities in 2024?

2025· dissertation· W7101898113 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typedissertation
Language
FieldPharmacology, Toxicology and Pharmaceutics
TopicPlant-based Medicinal Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismGovernment (linguistics)Corporate governancePoliticsPublic policy

Abstract

fetched live from OpenAlex

This research examines the marginalisation and socio-economic factors faced by Canadian Indigenous Peoples. There was a focus on the interconnected challenges in housing, land access, health care, education, financial security, political and public policy representations. To address this, a multi-method approach was undertaken, combining qualitative research with statistical analysis of national and specific regions’ data. Policy documents were reviewed to assess the extent of their priority with Indigenous causes. Attention was given to variations across regions, especially for those situated in the Yukon, Nunavut and Northwest Territories.While colonialism officially ended generations ago, impacts nonetheless continue to prevail. The Canadian government has made numerous public commitments to reconciliation, yet meaningful systemic change is yet to happen. Socio-economic barriers persist as a broader failure to eradicate colonial systems and to empower Indigenous governance through possibly self-governing bodies. Despite continued policy commitments, many communities continue to face unequal access to essential services as the marginalisation of Indigenous Peoples in Canada remains a deeply ingrained issue that has been shaped by generations of colonial influences as well as systemic neglect. This project’s analysis emphasises the need for deeply required socio-economic reform and battling marginalisation.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2240.001

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 head0.141
GPT teacher head0.426
Teacher spread0.285 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

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