Power and Norm-Setting in LSP: Anglicisms in the Language of Fashion Influencers
Bibliographic record
Abstract
The norm in languages for specific purposes, i.e. acceptability in terms of language use at all levels, from pronunciation and lexis to genre structure, is usually established in terms of the power over discourse exerted by user communities. However, in “non-hierarchical” or even “non-regulated” professions or areas of LSP, there is a gradation between lay and professional users which makes for boundaries that are moveable. “Influencers”, who use social media (Instagram, Twitter, etc.) to disseminate their ideas about fashion, are a case in point: they rarely (if ever) have any academic qualifications in fashion design, but may progressively become empowered, through their influence on social media, to determine language use in the language of fashion. The consequences of this empowerment of “outliers” would affect, amongst other components, the normativization of certain specialized languages: as influencers progressively approach the core of the profession, they may bring with them a host of borrowings—mainly Anglicisms in the case of Spanish influencers—which they then disseminate, in spite of what purists might argue. In our paper, we shall use the language of fashion as a case in point to show that the establishment of a norm is not necessarily subject to traditional hierarchies, and discuss the creation of (em)power(ment) dynamics which lead to visible results in language practices.
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 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.071 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".