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Record W4244347504 · doi:10.32920/ryerson.14649999.v1

The African Heritage Program: community members speak

2021· preprint· en· W4244347504 on OpenAlexafffundabout
Nomathemba Nkiwane

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
FundersGovernment of Ontario
KeywordsOppressionCurriculumPublic relationsSociologySocial connectednessThematic analysisGovernment (linguistics)RacismIdentity (music)Political sciencePedagogyQualitative researchPsychologyGender studiesSocial scienceSocial psychologyPolitics

Abstract

fetched live from OpenAlex

In this study, the extent to which African heritage is important in the schools under the Toronto District School Board is explored using the anti-racism and social constructivism frameworks. Phenomenology guided the research process and open ended questions were used to collect data. Thematic analysis was used to analyze the data. The major findings indicate that some of the needs of students of African descent were met through: identity affirmation; the integration of the African experience; reinforcement of culture and connectedness. The study contributes to anti-oppressive practices which stipulate that social programs and services should respond to multiple forms of oppression. Several recommendations that could potentially improve the program were made. These recommendations are: community based curriculum development and program delivery; the need for comprehensive government funding of the program and the need for the program to address contemporary gaps in skills development.

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)0.003
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 score0.034
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.001
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.004

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 head0.099
GPT teacher head0.416
Teacher spread0.317 · 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 routes3
Has abstractyes

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