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Record W7132862828

Divided by a Shared Language: Afrikaans and Raciolinguistic Projects in Post-Apartheid South Africa

2025· dissertation· W7132862828 on OpenAlexfundno aff
Rene Bogovic

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersUniversity of Toronto ScarboroughUniversiteit StellenboschUniversity of Toronto
KeywordsHegemonyEthnographyNegotiationPoliticsScholarshipNationalismAgency (philosophy)Power (physics)
DOInot available

Abstract

fetched live from OpenAlex

Afrikaans, a Dutch-based African semi-creole hijacked by Afrikaner nationalism and used as a tool of white supremacy under Apartheid, is at the centre of vociferous post-Apartheid contestations. Its racist past fuels ongoing negative perceptions and a shrinking public presence. Yet, as Afrikaners are losing their sole prerogative over the language, Afrikaans is increasingly mobilized for identitarian and political movements among its majority non-white speakers. Afrikaans thus offers a theoretically and politically generative opportunity to explore the imbrication of language and race in deeply racialized, unequal, and multilingual post-colonial spaces.This dissertation is an immersive ethnography of Afrikaans raciolinguistic boundary formation in post-Apartheid South Africa. By synthesizing race, boundary, and raciolinguistic scholarship, I examine how language constructs both hegemonic and marginalized racialized identities in a multilingual post-colonial space. I ask: how are Afrikaans language communities and racial boundaries co-constructed, maintained, and contested? How does Afrikaans remain marked as white? How do racialized Afrikaans speakers negotiate raciolinguistic identity? To situate contemporary negotiations of Afrikaans racial boundaries within historical and structural constraints, I draw on sociological literature on race (Bonilla-Silva 1997; Omi and Winant 2014) and group boundary scholarship (Brubaker 2002; Lamont and Molnár 2002; Wimmer 2013). To narrow the gap in theorizing language’s role in racialization, I build upon raciolinguistics (Alim, Rickford, and Ball 2016; Rosa and Flores 2017) to coin the concept of Raciolinguistic Project (RLP), a framework that accounts for both structural power dynamics and individual agency in leveraging Afrikaans to construct, maintain, and challenge racial boundaries. Drawing on immersive ethnographic fieldwork, focus groups, and interviews, I analyze how Afrikaans raciolinguistic entrepreneurs source sociolinguistic and historiographical materials from an erstwhile shared creolized continuum, to constitute two competing RLPs in dialogical tension: the White and Brown Afrikaans RLPs. Using creolization as an analytical and interpretative methodology, I identify common dimensions across projects. Despite producing white and Brown subjects who occupy vastly unequal positions of power and enjoy unequal access to institutional, symbolic, and social resources, both Raciolinguistic projects take language as the starting point for defining racialized group boundaries. Both the White and Brown Afrikaans RLPs appropriate Afrikaans to advance claims of indigeneity and historical rootedness; they claim linguistic marginalization to support a discourse of victimhood, often introducing religious or spiritual themes in framing associated forms of resistance; they seek to achieve authenticity by deflecting mixedness, mapped onto concerns of language purity and form; they brighten group boundaries in relation to both Afrikaans and non-Afrikaans speaking others. A latent creolizing undercurrent continually undermines the stability of the constituted raciolinguistic boundaries. This research foregrounds language in racial boundary making, expanding raciolinguistic literature by illustrating the dialogic co-construction of hegemonic and racialized identities through competing RLPs.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0430.033
Scholarly communication0.0080.007
Open science0.0010.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.456
Teacher spread0.405 · 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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