Bibliographic record
Abstract
Mixed methods research (MMR) based in a pragmatic research philosophy involves the integration of qualitative and quantitative methods to triangulate research findings and strengthen interpretations. This especially holds for complex research questions and/or data. Non-binary focused sociolinguistic research often deals with multiple complexities, including dynamic and contextually dependent ways of identifying and variation in body modification affecting speech production. While echoing prior calls for researchers to apply, when appropriate, a pragmatic/MMR framework (Angouri 2010), I uniquely argue that it can empower non-binary researchers and research collaborators, ultimately generating positive social change. My objective in presenting non-binary focused sociophonetic research is to demonstrate the framework’s advantages. These include foregrounding non-binary voices and experiences to generate rich, nuanced research questions, data, and analyses. These elements, as well as demonstrable ecological validity and multiple (collaborative and/or cross-discipline) perspectives are the hallmarks of transformative research which focuses on fostering social change.
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 | 0.092 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.072 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".