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Record W4312063463 · doi:10.32316/hse-rhe.v34i2.5083

Jarvis R. Givens, Fugitive Pedagogy: Carter G. Woodson and the Art of Black Teaching

2022· article· en· W4312063463 on OpenAlexvenueno aff
Jasmine Hawkins, Dionne Danns

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsArtVisual artsArt history

Abstract

fetched live from OpenAlex

education on the continent in general, and to move away from the exceptionalism that has tended to dominate educational history here" (13).The book is exceptional in its efforts at situating South Africa's colonial education history within the wide-ranging themes of domination, exploitation, oppression, and control, which characterizes the history of colonial education across other colonized parts of Africa.Another distinctive aspect of the book is the idiosyncratic lens through which the actions of individuals and numerous non-state actors are magnified and explored for their various roles in strategic historical events and outcomes.Peter Kallaway's The Changing Face of Colonial Education in Africa: Education, Science, and Development is an effort at forging an alliance between the South African educational history as well as the broader history of education across colonial Africa.Despite the differences in the education history of all colonizing powers and colonies, the common thread that runs through all is that it was a system that was founded on inequity, exploitation, oppression, and injustice.The work also focuses on both state and non-state actors including individuals in seeking to unearth the reality and relationship amongst policy formulation, implementation, and outcome.

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: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.009
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.091
GPT teacher head0.403
Teacher spread0.312 · 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
Published2022
Admission routes1
Has abstractyes

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