Divided by a Shared Language: Afrikaans and Raciolinguistic Projects in Post-Apartheid South Africa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.033 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".