Utterance-final tags in Aboriginal and non-Aboriginal English
Bibliographic record
Abstract
Once thought of as somewhat lacking ‘the precision and complexity of the central areas of grammar and phonology’ (Labov, 2013: 5), the field of discourse-pragmatic variation has recently blossomed (see Pichler, 2016). However, this expansion has been mostly restricted to mainstream linguistic varieties, and questions remain regarding the role of discourse-pragmatic features in signposting discourse (Author details).<br/>In this paper we offer the first analysis of utterance-final tags (UFTs) in Australian Aboriginal English (AAE), a contact-based variety of English spoken by an estimated 80% of First Nations people in Australia (Author details). UFTs are discourse- pragmatic features found at the end of utterances which perform multiple functions, including marking a proposition as common ground with the hearer (Denis & Tagliamonte, 2016: 87). UFTs are generally understood as being of two types: variable tags, which agree in person and number with the statement to which they are attached (e.g. isn’t it?) and non-variable tags (e.g. right?) which do not (Kimps, Davidse & Cornillie, 2014: 64).<br/>We draw on the speech of 30 speakers of AAE (14 men and 16 females) for whom AAE is their first language. The AAE corpus was collected using a Indigenous-led, cross-cultural, participatory methodology based on yarning – an Aboriginal form of storytelling and conversation (Author details). To facilitate cross-varietal comparison, we also consider narrative and sociolinguistic interview data collected with 30 native speakers of standarised AusE (16 men and 14 females). Both corpora were gathered in Nyungar country, Southwest Western Australia.<br/>Our distributional results indicate that both AAE and AusE strongly favour non- variable tags, and that – in line with Denis and Tagliamonte’s (2016: 97) findings for mainstream Canadian English – utterance-final you know is the most widely used variant across cohorts (example 1). Despite these similarities, while AusE markedly favours utterance-final you know (87% [150/172]), AAE makes use of a suite of UFTs, including you know (51%), eh (18%), yeah (11%), and unna (7%; example 2).<br/><br/>AAE speakers also use most of their UFTs in narrative orientation (AAE: 52% [34/65]; AusE: 11% [5/47]) which provides context for the main story line, while AusE speakers use them most in the complicating action (AusE: 61% [29/47]; AAE: 23% [15/65]). This pattern interacts with the use of UFTs such as you know as ‘quotative delimiter’ (Denis & Tagliamonte, 2016): AusE speakers use UFTs to accompany quotative re-enactment in their storytelling (29% [44/150]). In AAE yarning, quotation is not delimited by UFTs to the same extent (9% [22/231]). Instead, UFTs are deployed in narrative orientation to report facts (54% [126/231]) and confirm information (43% [35/80]).<br/>Malcolm (1994: 295) has argued that AAE speakers rely on ‘confirmation-seeking devices’ because, in AAE, communication is oriented to the group. Our results suggest that a group orientation does not only determine whether or which UFTs (‘confirmation-seeking devices’) will be used but also where in the yarning these will feature: AAE speakers use UFTs in narrative orientation to signpost their way into the main storyline, which is particularly important in the presence of non-Aboriginal interlocutors. UFTs, then, are a key element in the linguistic fabric of AAE yarning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".