MétaCan
Menu
Back to cohort
Record W1506975420 · doi:10.32396/usurj.v1i1.46

Canadian First Nations Child Welfare Care Policy: Managing Money in "Ottawapiskat"

2014· article· en· W1506975420 on OpenAlexaffvenueabout
Darcy Joseph Tootoosis

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWelfareNeglectPovertyGovernment (linguistics)IndigenousFoster careEconomic growthPolitical scienceSocial WelfarePublic administrationState (computer science)Development economicsEconomicsMedicineLaw

Abstract

fetched live from OpenAlex

The inter-generational loss of Indigenous identity in Canada has been a result of Canadian Aboriginal policy in the past and present. The policies of the residential school era and the policies of today’s child welfare system lead to similar outcomes, particularly governmental determination of how the next generation of First Nations people are affected by the state. By 1997-1998, the Department of Indian and Native Affairs reported that First Nations child and family services were administering services to 70% of children on reserves, and that number was projected to increase to 91% by 2002. In 1940 when the residential school system was still in full use, there were almost 8,000 children in the schools across the country; compare that statistic to the year 2002 when there were over 22,500 First Nations children in the child welfare system, showing a progression of almost three times the number living in state care. The numerous social problems resulting from poverty are re-enforced by the Federal Government’s policy decision to neglect taking action despite their own commissions and research data. Procedural problems in child welfare administration arise due to government jurisdictions, which will also be discussed.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0360.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.288
Teacher spread0.272 · 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.

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

Citations1
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueUSURJ University of Saskatchewan Undergraduate Research JournalSame topicIndigenous Health, Education, and RightsFrench-language works237,207