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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".