Transformative Research Methods to Increase Social Impact for Vulnerable Groups and Cultural Minorities
Bibliographic record
Abstract
A transformative lens applied to research increases impact in the form of providing support for actions that increase social, economic, and environmental justice. Researchers who accept the role of supporting transformative change can enhance their abilities to do so through the use of a transformative lens that informs the design, implementation, and use of their research. The transformative ethical assumption informs methodological choices in that the research design consciously focuses on addressing inequities and providing a platform for transformative change. Engagement with members of marginalized and vulnerable communities is critical and needs to be approached in ways that value the knowledge they bring and addresses power inequities. Methodologies that are commensurate with a transformative approach include the use of mixed methods, viewing the role of the researcher as a social change agent, learning from social activism, and employing specific strategies for culturally responsive inclusion, addressing power differences, and planning for sustainability. Examples of research that increased social impact illustrate how these methodologies have been applied: social activism strategies to address structural racism for youth and for Black men in prison; culturally responsive strategies in research affecting members of sexual minorities in countries in which same-sex behaviors are prohibited by law and for incarcerated women; power inequities in research for people living in high poverty, including children in Nicaragua and Indigenous South Africans; and planning for sustainability with Indigenous youth in Canada and farmers in South Africa. The transformative approach to research asks researchers to critically examine their role in sustaining an oppressive status quo and to address the challenges of supporting increased justice.
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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 | 0.156 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".