MétaCan
Menu
Back to cohort
Record W4405017094 · doi:10.1080/15332640.2024.2412031

Effects of the COVID-19 pandemic on Black African youth and homelessness in Toronto

2024· article· en· W4405017094 on OpenAlexaffabout
Dionisio Nyaga, Rose Ann Torres, Allison James

Bibliographic record

VenueJournal of Ethnicity in Substance Abuse · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsAlgoma University
Fundersnot available
KeywordsDiasporaSociologyGender studiesImmigrationAfrican americanNarrativeQualitative researchPolitical scienceEconomic growthSocial scienceAnthropology

Abstract

fetched live from OpenAlex

This qualitative narrative study investigates how social services among African immigrant youth in Toronto can be reimagined and provided in intersectional ways that are just and responsive to their specific and unique needs. The study interviewed 6 African Youths living in Toronto. The study employed an eclectic theory to argue for reimagining of policy that drive homelessness in Canada. The themes that came out of this study are: Homelessness is not African, The walls are squeezing me: Intersectional homelessness. African values and spiritualities are my survival tactic and policy resolution. The study calls all social work researchers and practitioners to work with African communities in providing social services that are attuned to African lived realities, values, and histories rather than relying on market-branded solutions for the "African problem," such as cultural competency frameworks that continue to mark and market African bodies for profit. The study employs an African-centered perspective to bring forth new approaches to African bodies in diaspora. The study looks at homelessness as a neoliberal concept intended to designate some bodies as improper and out of place while equally producing profit for the capital. Based on African immigrant youth narratives, homelessness is a foreign term in African cosmogonies since African people live with nature.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.241
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.413
Teacher spread0.343 · 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.

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Ethnicity in Substance AbuseSame topicHomelessness and Social IssuesFrench-language works237,207