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Education and employment opportunities among staff in Aboriginal family service agencies

2012· article· en· W1564803045 on OpenAlexaff
Jason Brown, Cheryl Fraehlich

Bibliographic record

VenueChild & Family Social Work · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaWestern University
Fundersnot available
KeywordsAgency (philosophy)Promotion (chess)CertificationService (business)Public relationsMedical educationPsychologySociologyPolitical scienceMedicineBusinessMarketingSocial science

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of the study was to describe ways that successful culture‐based Aboriginal preventive family service agencies offer employment and education opportunities for staff. Staff in three inner‐city, culture‐based Aboriginal family agencies were asked about their employment and educational opportunities. Forty‐four individuals were asked the question: ‘what employment and education opportunities have you had while in this job?’ A total of 81 unique responses were received. Participants grouped the responses into eight themes including: planning for services, promotion within the agency, specific skill development, enhanced self‐confidence, cultural awareness, teaching others, workshops as well as certified training. Differences between the experiences of study participants and the existing literature indicate that practices within culture‐based Aboriginal family agencies are distinct in relation to funding, staff mobility, strengths‐base, practical training and cultural knowledge, and that these should be understood and recognized formally in funding decisions and in future research.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.036
GPT teacher head0.316
Teacher spread0.280 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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