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Record W2921935644

Uncovering a Love of Self: Individuality and Coloured Identity in Ntokozo Madlala and Mandisa Haarhoff’s Crush-hopper

2018· article· en· W2921935644 on OpenAlexaffvenue
J. Coplen Rose

Bibliographic record

VenuePostcolonial text · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsAcadia University
Fundersnot available
KeywordsIdentity (music)State (computer science)Gender studiesEthnic groupFace (sociological concept)Independence (probability theory)SociologyWhite (mutation)DemocracyRace (biology)RacismPolitical scienceAestheticsLawPoliticsAnthropologyArtSocial science
DOInot available

Abstract

fetched live from OpenAlex

Ntokozo Madlala and Mandisa Haarhoff’s Crush-hopper (2011) explores the challenges that historically oppressed South Africans face when attempting to define themselves outside, or beyond, the racial categories imposed by the former apartheid state. Although apartheid laws that arbitrarily divided citizens into distinct racial categories ended with the creation of a legal system “committed to non-racialism” at independence, in many instances people continue to feel trapped by past definitions of race (Desai and Vahed 1). Pallavi Rastogi asserts this crisis is especially felt by ethnic minorities such as South African Indians, some of whom feel the decolonizing state is “still predicated along the black and white binary” that was entrenched during apartheid (550). Exploring the impact that this racial polarity has had on a citizen of mixed heritage raised outside of apartheid’s temporal limits, Crush-hopper illustrates the multiple ways that racial hierarchies imposed on marginal communities during colonization continue to cause trauma and a fractured sense of identity after the nation’s transition to a democratic state.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.302
Teacher spread0.286 · 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 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
Published2018
Admission routes2
Has abstractyes

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