Integrating qualitative and quantitative analyses of stance: A case study of English<i>that/</i>zero variation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Previous work has shown that stance—the way speakers position themselves with respect to what they are talking about and who they are talking to—provides powerful insights into why speakers choose certain linguistic variants, beyond correlations with macro-social categories such as gender, ethnicity, and social class. However, as stancetaking moves are highly context-dependent, they have rarely been explored quantitatively, making the observed variable patterns difficult to generalize. This article seeks to contribute to this methodological gap by proposing a formal guide to coding stance and demonstrating how it can be operationalized quantitatively. Drawing on a corpus of eight individuals, self-recorded in three situations with varying levels of social distance, we apply this method to variation between English complementizers that and zero (i.e. no overt complementizer), providing a replicable and theoretically grounded protocol that incorporates both quantitative and qualitative analyses in a variationist sociolinguistic study. (Stance, complementizers, that , English)*
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it