“View and Hide Definitions” of Racist Hate Speech: Ethnophaulisms in Google’s English Dictionary
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.108 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it