Ó ‘clábar’ go ‘clobber’ agus an t-athrú cló: forás brí ainmfhocail de bhunús Gaeilge i dTalamh an Éisc le himeacht ama
Bibliographic record
Abstract
Agus ré na teicneolaíochta linn bíonn gá le téarmaí nua a chumadh go rialta le dul i ngleic le riachtanais teanga na sochaí: focail ar nós ‘féinín’ agus ‘clicmhealladh’, mar shampla. Léirítear riachtanais teanga éagsúla a thagann chun cinn ó ghlúin go glúin agus an saol ag athrú. Anuas air sin, is minic a bhíonn focail iasachta fite fuaite i dteangacha agus iad á n-úsáid go nádúrtha ag an bpobal labhartha: leithéidí tsunami, avatar, sudoku, cul-de-sac, cuirim i gcás. Is díol spéise go nglactar leis na leaganacha iasachta sin de réir a chéile, rud a léiríonn an meascadh cultúrtha a tharlaíonn beagnach i ngan fhios don phobal labhartha agus an chaoi a mbíonn teangacha ag athrú de shíor dá bhrí sin. Ní heisceacht í an Ghaeilge agus í ag athrú leis na cianta. Is é aidhm an ailt seo, mar sin de, díriú ar athrú céille ainmfhocail de bhunús Gaeilge i dTalamh an Éisc mar shampla ar leith den phróiséas athraithe sin. Déanfar iniúchadh agus anailís ar raon samplaí atá le fáil sa Dictionary of Newfoundland English (Story et al., 1982) trí lionsa na staire agus na teangeolaíochta, chun patrúin shuntasacha éagsúla a thabhairt chun solais maidir leis an bhforás brí a tharla do na focail sin le himeacht ama.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.038 |
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".