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Record W2904057167 · doi:10.4324/9781315656854-4

Working with Indigenous Families

2017· book-chapter· en· W2904057167 on OpenAlexaboutno aff
Donna Hartz, Leona McGrath

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGeographySociologyBiologyEcology

Abstract

fetched live from OpenAlex

In Australia, Aotearoa New Zealand and Canada, the impact of colonisation has resulted in profound disparities in health and social well-being between the Indigenous and non-Indigenous populations. This chapter discusses the perinatal health of Indigenous women and babies with a focus on three countries: Australia, New Zealand and Canada. It emphasises the impact of colonisation on the health of Indigenous populations and describe disparity in social and health outcomes for Indigenous peoples compared with non-Indigenous people including perinatal outcomes. The chapter discusses the key characteristics of positive policy and service responses and identifies the strategies aimed at promoting psychosocial resilience and optimising health outcomes for Indigenous childbearing women, their babies and families in these countries. Social reforms introduced in 1988 based on the three principles of the Treaty of Waitangi, now underpin many social and health reforms in New Zealand - and unlike other Indigenous peoples discussed in this chapter, New Zealand is a truly bicultural nation.

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)0.003
metaresearch head score (Gemma)0.004
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: Other
Teacher disagreement score0.092
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0260.007
Scholarly communication0.0050.006
Open science0.0020.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0300.005

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 head0.037
GPT teacher head0.289
Teacher spread0.252 · 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
Published2017
Admission routes1
Has abstractyes

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