MétaCan
Menu
Back to cohort
Record W2408722346 · doi:10.7202/1077181ar

Lessons Learned from the Yellowhead Tribal Services Agency Open Custom Adoption Program1

2021· article· en· W2408722346 on OpenAlexaffvenueabout
Jeannine Carrière

Bibliographic record

VenueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First Peoples · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAgency (philosophy)IndigenousPublic relationsService (business)Political scienceDoorsPublic administrationBusinessSociologyEngineeringMarketingSocial science

Abstract

fetched live from OpenAlex

Following a historic meeting of staff with Alberta Children’s Services and the Yellowhead Tribal Services Agency (YTSA), a pilot program, the YTSA Open Custom Adoption, was developed. The agency initially researched existing adoption models in the Northwest Territories, British Columbia and in the Cheyenne Nation in the United States. An advisory committee, comprised of one Elder from each member First Nation community, was asked to provide guidance and direction throughout the project. From 2000 to 2010, YTSA placed over a hundred children in adoptive homes without any adoption breakdowns (Peacock & Morin, 2010). Although the agency has now closed its doors, there are lessons to be learned from the YTSA Open Custom Adoption program which is still viewed as an advanced model of adoption service inspired by traditional First Nation teachings and child caring. This article is a review of lessons learned from this agency and in particular, the importance of connectedness to family, community culture and nationhood for Indigenous children and adoption.

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)0.015
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0020.005
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 head0.046
GPT teacher head0.365
Teacher spread0.319 · 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
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First PeoplesSame topicChild Welfare and AdoptionFrench-language works237,207