Bibliographic record
Abstract
Abstract The paper describesanywayin the Irish component of the International Corpus of International English (ICE-IRL) and similarities and differences in its use compared with other varieties of English represented in the ICE-Corpus. The findings showed thatanywaywas most frequent in ICE-IRL closely followed by the Canadian component of the ICE-Corpus. It was least frequent in the Santa Barbara Corpus of Spoken American English. The present analysis has demonstrated thatanywayin Irish English has a distinctive pragmatic profile which is based on its position in the peripheries and what it is doing there.Anywayin the left periphery was placed after an intrusive topic, a digression, a correction, interruption by the hearer and permits the speaker to resume the topic in an explicit way. In Irish Englishanywayin the right periphery was above all an attenuating politeness marker.Anywaywas found with a hedging meaning in contexts where it restricted the validity of a proposition by making a correction or adjustment of some kind. The high frequency ofanywayin the right periphery as a hedging pragmatic marker can be analysed in terms of its capacity to index a special kind of Irishness characterized by the absence of self-promotion and avoidance of social distance
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".