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Record W7132907675

Beyond the Barriers Black-Led Non-Profits and their Impact on Life Outcomes of Black Youth

2025· dissertation· W7132907675 on OpenAlexaff
Amina Warsame

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsCanadian Association for the Study of Adult Education
Fundersnot available
KeywordsRacismBlack womenWork (physics)Life chancesBlack femaleBlack maleDiversity (politics)Ethnic group
DOInot available

Abstract

fetched live from OpenAlex

Systemic racism and racial inequities faced by the Black community cause barriers in individual and communal advancement. Black-led non-profits play a pivotal role in filling these gaps by providing essential needs such as shelter, food, employment and resources. By drawing on articles focused on mentoring and educational programs within the Black non-profit sphere in North America, this master’s thesis reviews literature on Black-led non-profits and their impact on life outcomes of Black youth. Part I draws on the significance of Black non-profits and how they work to resist anti-Black racism. Part II delves into literature and case studies on the unique impact of Black non-profits on Black youth educational and well-being outcomes. This thesis emphasizes the importance of Black-led non-profits in enhancing Black youth outcomes through culturally relevant programming which can be helpful to educators, policymakers and community leaders working within the Black community.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.404
Teacher spread0.373 · 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
Published2025
Admission routes1
Has abstractyes

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