Bibliographic record
Abstract
This lecture will be of interest to anyone who wishes to know more about the relationship between language and culture. \nGo to Seeing the world as an African language speaker \n \nIs the way we see the world influenced by our language, or is it the other way around: is our language influenced by the way we see the world? You’ve all heard (the rather faulty) example of the Inuit having many words for snow – and maybe even have heard of the South American language called Yagan with a word Mamihlapinatapei which refers to the desirous look two people give each other when they want to start something but are too hesitant to do so (what a wistful, romantic culture, you might think!) But what about here in South Africa? What is it about African languages that makes them uniquely different and astonishingly original in the way they are put together? This lecture will introduce you to some of the key features (both structural and metaphorical) of our languages, features essential to understanding their cultures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".