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Record W4379114517 · doi:10.5296/ijsw.v10i1.20952

Racial Equity for Culturally Specific Organizations: An Assets-Based Evidentiary Assessment

2023· article· en· W4379114517 on OpenAlexaff
Ann Curry‐Stevens

Bibliographic record

VenueInternational Journal of Social Work · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEquity (law)Public relationsValuation (finance)BusinessMarketingPolitical scienceAccounting

Abstract

fetched live from OpenAlex

Culturally specific organizations have been missing out on the racial equity and cultural responsiveness initiatives that have been prominent in nonprofits and government, both due to the absence of relevant assessment tools, as well as discourses within these initiatives which have overlooked the sector of culturally specific organizations and subsequently provided a competitive advantage to culturally responsive organizations. This paper reports on the development of a culturally specific organizational assessment, and the results of a four-site pilot study of the tool. The assessment tool is based on the results of a Delphi and Consumer Voice two-part study into the assets of culturally specific organizations and their valuation by clients. Developed with the input of four organizations serving a range of communities of color, we have confirmed that the Successful Families Tool achieves its objectives (centering primarily on creating actionable insights and quality improvement plans). Additional benefits emerged from the pilot’s participants: that an evidence-base for the organization’s assets is initiated, that organization-wide identity is strengthened, that staff have been energized by relevant equity dialogues and relationship deepening, and that motivation for research and evaluation that captures the fullness of the organization’s impacts be undertaken. A sampling of tool questions and standards is included, along with listing of its ten domains.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.460
Teacher spread0.376 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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