A corpus-based comparative pragmatic analysis of Irish English and Canadian English
Bibliographic record
Abstract
This PhD thesis is a comparative study of the spoken grammar of Irish and Canadian Englishes within the framework of Variational Pragmatics at the formal level, used to study the pragmatic variation (the intra-varietal differences) in terms of forms and pragmatic functions. It is a study of spoken grammar as a whole (in a comparative and representative way between and across two varieties of English). Corpus linguistics is used as a methodological tool in order to conduct this research, exploring the nature of spoken grammar usage in both varieties comparatively in relation to their pragmatic functions and forms. The study illustrates an iterative approach in which top-down and bottom-up processes are used to establish pragmatic markers and their pragmatic functions in spoken grammar in the two varieties. Top-down analysis employs a framework for spoken grammar based on existing literature while the bottom-up process is based on micro-analysis of the data. The corpora used in the study are the spoken components of two International Corpus of English (ICE) corpora, namely ICE-Ireland and ICE-Canada comprising 600,000 words each (approximately). Methodologically, this study is not purely corpus-based nor corpus-driven but employs both methods. This iterative approach aligns with the notions of corpus-based versus corpus-driven linguistics and perspectives. Corpus tools are used to generate wordlists of the top 100 most frequent word and cluster lists. These are then analysed through qualitative analysis in order to identify whether or not they are a part of the spoken grammar. This process results in a candidate list that can then be functionally categorised and compared across varieties in terms of forms and functions. Specifically, the study offers insights on pragmatic markers: discourse markers, response tokens, questions, hedges and stance markers in Irish and Canadian English. The results offer a baseline description of the commonalities and differences in terms of spoken grammar and pragmatics across the two varieties of English which may have application to the study of other varieties of English. Also, the prominent forms of spoken grammar across these two varieties can be further explored from a macro-social perspective (e.g. age, gender, or social class) and a micro-social perspective (e.g. social distance or social dominance) and how these interplay with pragmatic choices.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".