“View and Hide Definitions” of Racist Hate Speech: Ethnophaulisms in Google’s English Dictionary
Bibliographic record
Abstract
This paper aims to foster debate about the language of racist hate speech in online English lexicography. For this purpose, it presents a study on the treatment of ethnophaulisms, or ethnic slurs, in “powered by Oxford Languages” Google’s English dictionary. The focus is indeed on the perspective of the general user of the Internet, in light of the connection between two facets of this digital age. The first one is the strong and growing tendency among Internet users to ‘google’ their language issues. The second one is the alarming increase in cases of hate speech online, most of which are based on ethnicity and nationality, according to reports by the United Nations. Consequently, the free and pervasive content of Google’s English dictionary represents a case in point to investigate whether and how online users are warned against the power of these hate words. A selected sample of 285 English ethnic slurs have been looked up in the dictionary and, if recorded, their entries have been scrutinised to identify lexicographic data regarding their semantic relevance and offensiveness. Findings show that the majority are included, they mostly present ethnicity-related senses, but less than half of the total are treated as ethnophaulisms. In this respect, the major dictionary markers indicating offensiveness are effect labels, predominantly alone or combined with definitions. Relative to their size, thus, ethnophaulisms in Google’s English dictionary are clearly described as offensive or derogatory expressions, thus making online users aware of their hurtful nature.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".