Structural-Semantic Peculiarities of Derogatory Marked Ethnonyms of the Canadian, Australian and New Zealand English Language
Bibliographic record
Abstract
The article studies word formation of the derogatory marked ethnonyms (DME) of the Canadian, Australian and New Zealand English Language. DME are classified according to the method of word formation, the type of semantic transfer and deliberate phonetic distortion. Selection of the analyzed units is made out of such lexicographical sources as online dictionary of colloquial vocabulary Urban Dictionary, online dictionary Oxford English Dictionary, Merriam-Webster Online: Dictionary and Thesaurus, ABBYY Lingvo, The Free Dictionary, Dictionary.com, electronic databases The Racial slur Database and Hatebase, lists of ethnonyms from online resources canadaka.com, fact-index.com and other sources of factual materials. The urgent character of the given article is caused by lack of scientific study of the ways of word formation of DME, particularly, the units of Canadian, Australian and New Zealand English. Separation of the criteria of their description and division into groups are considered to be important. The aim is to justify the linguistic phenomenon of DME through determining their structural and semantic characteristics in Canadian, Australian and New Zealand English. Achievement of the aim requires solving the following problems: 1) to identify the structural and semantic parameters of formation of DME; 2) to improve the structural and semantic classification of A.I. Hryshchenko for Canadian, Australian and New Zealand English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".