Combining Scientific Worldviews in Mixed Methods Research
Bibliographic record
Abstract
Graduate students and novice researchers can face scientific worldview-related stereotypes, stigmatization, and disruptive tensions during mixed methods research meetings. To avoid such difficulties, there is a need to better understand how to use and combine several worldviews in the same mixed methods study. Yet, little is known on ‘how to' combine worldviews. In this chapter, the authors report their literature review of key reference texts and a sample of mixed methods empirical studies. Key findings show five common contemporary worldviews (postpositivism, social constructivism, pragmatism, critical theory, and critical realism) and six possible combinations of worldviews in mixed methods studies: three combinations without integration (a-paradigmatic, substantive theory, and single worldview) and three combinations with integration (multiple worldviews, complementary strengths, and dialectical pluralism). This led to propose a framework and aid for combining worldviews to help team members to prevent and manage worldview-related disruptive tensions in their mixed methods projects.
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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 | 0.163 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".