These Lions Speak Zulu Too: Exoticising African Languages
Bibliographic record
Abstract
If the fact that there are only 27 of us in the entire world is not impressive enough, we also speak Zulu. (from a 1995 subway poster depicting the white lions at the Metro Toronto Zoo) Initially, when I read the above quote, a number of questions went through my mind and, despite my many attempts to see the humour intended by such an ad, I was overcome with anger, humiliation, and frustration. As a South African Zulu speaker living in Canada, the image of Africa that faced me then had neither humour nor the exotic sense it was meant to have. The presentation of Africa in Canadian media has more often taken two diametric persuasions. On the one hand, there is the image of starving children, poverty, and war. On the other hand, there is the picture of Africa as an endless land of wonders, of exotic people, and amiable animals who can even speak exotic languages. Certainly, the ad under discussion falls within the latter trend; that is, the white lions are worth seeing because they are exotic. There are only 27 of them and they can talk, not just any language, but the exotic Zulu. As a Zulu speaker, I do not find anything exotic in the knowledge and use of the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".