Bibliographic record
Abstract
Abstract Qualitative researchers are increasingly called on to take part in research teams with complex mixes of disciplinary, methodological, and global connections. Unfortunately, many are not well prepared to work in these circumstances. Moreover, the leaders of these teams often lack knowledge of the ways qualitative researchers could enrich team processes through the unique characteristics of qualitative research and the special skills those trained in this methodological approach could bring to the project. Social scientists have few curricular materials or instructional models to guide them in learning how to better integrate qualitative researchers on team research projects. This goal of this chapter is to bring attention to this issue and raise key concerns that will require attention to address this issue. The chapter provides an overview of the small body of research that has developed in regard to qualitative research and complex teams and then raises a set of four key issues that need attention: (a) making the best use of qualitative research on a team; (b) selecting digital tools; (c) attending to new developments in writing; and (d) addressing issues of social justice. Also included is a discussion of the curricular issues that must be considered as the field moves forward to consider the ways qualitative researchers can best enter this new world of complex teams.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".