Some Problems in Recording and Analyzing South African English Vocabulary (The Experiences of an Outsider)
Bibliographic record
Abstract
This article describes some problems in collecting and studying South African English vocabulary on the basis of non-South-African texts faced by a linguist who is a native speaker of American English. The questions are thus: Are non-South-African texts just as reliable as South African texts? More reliable? Less reliable? And is a linguist who is a native speaker of a different variety of English just as reliable as a native? More reliable? Less reliable? It is suggested here that the best way of studying a language, if possible, is by having both insiders and outsiders look at the material. Keywords: abbreviations, african languages, afrikaans, american english, australian english, black english, british english, canadian english, capitalization, careful use of primary and secondary sources, convergence, definitions, dictionaries, differential dictionaries, dutch, english, etymology, family names, folk etymology, french, german, hebrew, initialisms, latin, lexicography, misprints, nonce forms, overdefinition, personal names, place names, postal terms, prepositions, productivization, reflexive pronouns, slang, slips of the tongue, south african english, spelling, status and usage labels, surfers' terms, teamwork, underdefinition, yiddish, zoological terms
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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 | 0.067 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".