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Record W2971027938 · doi:10.5539/ijel.v9n5p107

GD Usernames and Euphemism: A Morphosemantic Analysis

2019· article· en· W2971027938 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSwearing, Euphemism, Multilingualism
Canadian institutionsnot available
FundersUniversity of Portsmouth
KeywordsEuphemismComputer scienceLinguisticsParalanguageNatural language processingArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Gamedesire (GD), a free online gaming website, is a rich resource for language research on Computer-Mediated Communication (CMC). GD raises a number of linguistic inquiries on written English. This paper analyzes the morphosemantic mechanisms of forming euphemistic GD usernames. A dataset of two hundred usernames has randomly been selected and tested against Warren’s (1992) model. The study demonstrates that a plethora of GD usernames carry dysphemistic connotations that are denotatively euphemized with linguistic and paralinguistic mechanisms, including word formation, orthographic modification, borrowing and semantic innovation. Some of the dataset usernames could not be subsumed under the selected model, necessitating the addition of new devices and the development of a new rendition of the model. The study reveals that GD users employ several processes for creating their usernames, which are characterized by grammatical, lexical, phonological, graphological, and semantic deviations from language norms.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.324
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it