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Record W4240914571 · doi:10.32920/ryerson.14647641

Relationship-based child protection: practice informed by indigenous social work students in the Northwest Territories

2021· preprint· en· W4240914571 on OpenAlexaffabout
Tasha Lake

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityLaurentian UniversityCentre for Social Innovation
Fundersnot available
KeywordsEthnographyIndigenousContext (archaeology)WelfareSocial workSociologyWork (physics)Field researchScope (computer science)Perspective (graphical)Field (mathematics)PerceptionPublic relationsPedagogyPolitical scienceGeographySocial sciencePsychologyArchaeologyAnthropologyEngineeringLawVisual arts

Abstract

fetched live from OpenAlex

This study explores Ingenious perspectives of relationship building and how this perspective might be adapted into a child welfare context. The study was born out of my experience working in a child welfare in the community of Yellowknife, Northwest Territories. The theoretical framework draws from an Anti-Colonial perspective and the research methodology was adapted from critical ethnography to fit the scope of the research project. The sample includes 4 diploma of social work students from Aurora College in Yellowknife Northwest Territories as well as field notes form my personal journals from when I lived in the community and field notes from a data collection trip to Yellowknife, Northwest Territories in the Spring of 2014. Findings provide community perceptions of social workers, community standards, a process of relationships-based practice and the benefits to this practice style. Barriers to relationship-based practice are also identified as an area for further exploration.

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)0.010
metaresearch head score (Gemma)0.010
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 score0.924
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0190.016
Scholarly communication0.0060.004
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.388
Teacher spread0.344 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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