Mixed-Methods Approaches to the Study of Language Attitudes
Bibliographic record
Abstract
This chapter examines what research falls under the epithet of ‘mixed-methods’ and discusses the main advantages of conducting mixed-methods research. The chapter introduces the key issues of mixed-methods research planning and design: that is, the tackling of ontological and epistemological challenges, the equal weighting of methods, and the sequencing of methods. The chapter also provides information regarding the analysis of data resulting from mixed-methods research, and how this can be done in a manner that provides appropriate integration. The key issues of the chapter are illustrated by means of two case studies. The first investigates attitudes towards French and English in Montreal, making use of a questionnaire and a matched-guise experiment. This case study shows how mixed-methods approaches can lead to a deeper understanding of language attitudes as part of larger social processes in a manner that no one method on its own could do. The second case study examines attitudes towards Catalan in Northern Catalonia by means of interviews and variable analysis. This case study demonstrates how mixed-methods research allows for a broader representation of the attitudinal and ideological landscape of a specific language community than could be afforded by the use of one method alone.
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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 | 0.095 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".