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Record W2136888500 · doi:10.22230/cjc.2010v35n1a2216

The Demonization of Aboriginal Child Welfare Authorities in the News

2010· article· en· W2136888500 on OpenAlexaffvenue
Robert L. Harding

Bibliographic record

VenueCanadian Journal of Communication · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsDemonizationWelfareAgency (philosophy)Public relationsScarcityChristian ministryPolitical scienceCompetence (human resources)Social workSociologyPsychologySocial psychologyLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

This study compares news representations of Aboriginal child welfare agencies with those of provincial authorities such as BC’s Ministry of Children and Family Development. News coverage of critical incidents involving children under the care of provincial bodies focused on systemic problems such as programs cuts, scarcity of resources and organizational deficiencies—conditions over which individual social workers had little control. In contrast, these contextual factors were largely absent from reportage of delegated Aboriginal agencies. Instead, most news reports and opinion pieces focused on blaming Aboriginal social workers and agency officials as well as questioning the competence of Aboriginal service providers in general. On the other hand, opinion pieces written by Aboriginal people introduced structural issues and contextual factors not included in the news.

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)0.007
metaresearch head score (Gemma)0.027
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 score0.917
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.007
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.344
Teacher spread0.327 · 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

Citations13
Published2010
Admission routes2
Has abstractyes

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