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Record W4249978643 · doi:10.32920/ryerson.14663433.v1

Stories and strategies of resistance: multi-stakeholder advocacy efforts in publicly-provided home support services in Ontario.

2021· preprint· en· W4249978643 on OpenAlexaffabout
A. Connolly

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsToronto Metropolitan UniversityLakehead UniversityCentre for Social InnovationDalhousie University
Fundersnot available
KeywordsAllianceStakeholderResistance (ecology)Public relationsContext (archaeology)NarrativePolitical scienceWork (physics)Stakeholder engagementSociologyEngineering

Abstract

fetched live from OpenAlex

This Major Research Paper conducted analysis of narrative interviews that explored the experiences of individuals involved with a multi-stakeholder advocacy group, Quality Care Alliance. During its period of operation, the Alliance advocated around intersecting issues facing home care workers, service users, and family members. The research sought to learn about the efforts of QCA and experiences of its members, in terms of the enabling factors, successes, barriers and challenges faced. This research aims to contribute to knowledge about multistakeholder advocacy within the context of neoliberalism. Six themes were uncovered around: participants’ roots of involvement in advocacy, (dis/non)engagement, making solo struggles shared, value of connections and relationships, group processes and challenges related to the work. Anti-oppressive social work practitioners could benefit from supporting advocacy efforts that involve diverse stakeholders, and employing decolonizing practices while engaging in such efforts, especially within the constraints of a neoliberal context.

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)0.009
metaresearch head score (Gemma)0.026
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 score0.584
Threshold uncertainty score0.828

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0330.019
Scholarly communication0.0080.006
Open science0.0040.013
Research integrity0.0040.006
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 head0.096
GPT teacher head0.370
Teacher spread0.274 · 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 routes2
Has abstractyes

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