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Record W4230390992 · doi:10.32920/ryerson.14654595.v1

Black wealth mobility

2021· preprint· en· W4230390992 on OpenAlexaff
Tasha Sinclair Riley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of WindsorToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsSpace (punctuation)SociologyStorytellingRacismRace (biology)NarrativeCritical race theoryQualitative researchPublic relationsSocial workSocial mobilityGender studiesPolitical scienceSocial scienceEconomic growthComputer scienceEconomics

Abstract

fetched live from OpenAlex

This is a narrative storytelling qualitative research study on Black wealth mobility. Through a Critical Race Theory and Anti-Black Racism lens, this study allows the experiences of Black social service workers to help understand the route and tools used when navigating wealth mobility, and creating a separate space to define the Black experience throughout this process. Existing research shows there are significant gaps in attaining wealth for Black communities, and very little surrounding solutions for these gaps. As social service providers, participants were able to not only make suggestions for social supports to be developed, but also to utilize counter-storying telling to pinpoint issues existing within the current social sector which also contribute to these gaps in Black communities. This research not only gave a space for Black bodies to express and share their experiences, but also a space to critically reflect on the work done in these communities.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.004
Scholarly communication0.0030.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.051
GPT teacher head0.382
Teacher spread0.331 · 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 designObservational
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 routes1
Has abstractyes

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