The Art of Becoming a Minority: Afrikaner Re-politicisation and Afrikaans Political Ethnicity
Bibliographic record
Abstract
The accord to formally end apartheid did not bring an end to efforts advocating the preservation and promotion of Afrikaans as a language, a culture and a family of identities and communities. One strand of recent studies treats these efforts analytically as nationalist projects, implying that any preoccupation with power to protect cultural and linguistic practices constitutes a revival of Afrikaner nationalism. In this conceptual article, we propose to distinguish between political ethnicity and nationalism, arguing that the notion of political ethnicity is better suited to analyse contemporary ethnopolitical demands than nationalism. Whether there is a (hidden) long-term intent of creating a self-determined Afrikaner nation should not be presupposed but be an empirical question in each case studied. Departing from a discussion of Mariana Kriel's perspective on Afrikaner nationalism, we develop an understanding of political ethnicity and discuss its relation to race and nationalism. As current ethnopolitical efforts are entangled with the past, we analyse the conceptual legacy of the former hegemonic Afrikaner nationalism with regard to what we call its bicameral ontology and propose a different understanding of social entities, questioning the adequacy of sustaining split ontologies in what appears to be a more diverse social environment than ever. Empirical research, we suggest, should also consider the innovative, creative and exploratory aspects of what we think should be studied as one of the more intriguing and politically puzzling contemporary attempts at becoming a minority.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.029 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".