How to Encourage Inclusion in a Qualitative Research Project Using a Design-Based Research Methodology
Bibliographic record
Abstract
Many issues and challenges face research design and research teams that want to become more inclusive, especially in large-scale research projects involving many stakeholders. This article explores an approach called Design-Based Research (DBR). DBR has been widely used in education for several years; it emphasizes collaboration with the community and takes the context of participants into consideration. DBR is transposable to other disciplines and is intended to be inclusive of the diverse stakeholders involved in a research project. For instance, in an ongoing research project about unconscious bias and inclusive behaviors, it takes into account all stakeholders’ needs and involves them in all stages of the research, which is taking place in a real-world context rather than a laboratory. The aim of this article is to better understand how the DBR methodology enables the inclusion of historically marginalized groups and how it is applied in the field. This exploratory article will present an example of an ongoing research project using the DBR methodology to show how this approach can be more inclusive than experimental approaches. This exploration reveals the positive impact of DBR in implementing solutions that can help reduce inequalities and power relationships. It also reveals the complexity of conducting qualitative research in a social laboratory. In particular, it takes into account the specificity of each historically marginalized group, from an intersectional perspective, the difficulty of operating within a process where not everything is determined in advance, and the need for a researcher specializing in DBR. It is important to allow sufficient time and financial resources at each stage to recognize the involvement of community organizations. The tools and knowledge generated by this type of research project will be useful for other organizations and future research.
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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 | 0.530 | 0.519 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".