Bibliographic record
Abstract
Since the early 1990s, mixed methods research (MMR) has evolved into the third research paradigm alongside quantitatively oriented inquiry (numeric data) and qualitatively oriented inquiry (narrative data). MMR is interested in both quantitative and qualitative analyses and primarily works from a pragmatic stance to use what works best in order to answer research questions. The research question, rather than a preconceived paradigm (e.g., post‐positivist, positivist, constructivist), is central and drives the choice of design. The interest in and practice of MMR have emerged rapidly and spread across many domains in the social and behavioral sciences. Within this context, the focus of this chapter is on discussing MMR as it has manifested itself within the language testing/assessment (LT) research community. In some ways MMR's evolution in LT parallels its general emergence, but in other ways there are developments specific to the LT field. For example, MMR is increasingly being employed for instrument development, classroom‐based assessment, and large‐scale assessment studies. In addition, MMR designs are being used to address issues such as construct definition and rater effects in language assessments. To help explain this evolution, the chapter situates MMR from an historical perspective and discusses how LT research designs are increasingly situating themselves in this third research community. As this trajectory is traced in LT research, the chapter also describes the nature, conventions, practice, and research designs that are emerging and becoming specific to the LT field. It is interesting, however, that any debate or discussion of MMR issues seems less prevalent in LT, whereas it is abundant in the general MMR literature. What is important to note is that LT's rationale for the use of MMR follows closely the philosophical orientation most often associated with it, that is, pragmatism.
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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 | 0.099 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.085 | 0.022 |
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".