Corpus Linguistic Methodology as an Advanced Conversion Design for Social Science Research
Bibliographic record
Abstract
This article offers a corpus linguistics methodology (i.e., guiding the collection of language-based qualitative data, queried with both qualitative and quantitative methods, and mixed analytical approaches) as a unique and practical advance in the conversion mixed methods design. The conversion mixed methods design takes qualitative or quantitative data, and reforms, queries, or analyzes that data using a converse qualitative, quantitative, or mixed method. I argue that the design is uniquely suited to maximize the interlinking of language-based data within strands, as well as in cross-strand comparison. The article focuses on several concepts in corpus linguistics, specifically collocation (i.e., multiple words appearing together), semantic preference (i.e., the tendency of those words to appear in certain contexts), and semantic prosody (i.e., the sociocultural meaning of those words in that context), and covers both conceptual background and procedures for carrying out analyses. Further, the approach demonstrates the utility of more fluid design description by focusing on the timing and purpose of integration. I articulate this argument by outlining a study on the opioid epidemic in elderly health services in rural Pennsylvania, with the help of a zipper metaphor to describe the design. The article concludes with a discussion of the value of this advanced design to mixed methods researchers representing the social sciences outside of linguistics-centered disciplines, at a methodological level, as well as the ready availability of tools that allow researchers to instrumentalize the design.
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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 | 0.170 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".