Bibliographic record
Abstract
Discussions relating to Irish English and the media invariably focus on how the media are transforming or, indeed, threatening the way the English language is spoken in Ireland. Newspaper articles bearing headlines such as ‘Leave upspeak to the, like, Americans?’ (Behan 2005) or ‘Janey Mac! Irish-English is banjaxed, so it is …’ (Bielenberg 2008) tend to lay the blame for any change to Irish dialects squarely on the media. Although an examination into such claims would certainly be interesting, and similar research in the Scottish context has already been conducted by Stuart-Smith and Timmins (2014) in Sociolinguistics in Scotland , this is not the approach that will be taken here. Like Coupland in his influential ‘The mediated performance of vernaculars’, I, too, believe that it is unnecessary ‘to limit the study of mediated dialect to a “vitality” agenda (“Will the mass media keep dialects alive or kill them off?”) or to a “media effects” agenda (“Do the mass media influence the course of language change?”)’ (Coupland 2009: 297). Instead, it is possible to see the media as holding a mirror up to society and to examine language in film as evidence of art imitating life rather than vice versa. Thus, rather than exploring to what extent the media shape Irish English (hereafter IE), this chapter will instead examine to what degree the media reflect vernacular usage. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".