Embedding Intersectionality Values in Citizen Science Research: The Socio-Scientific Progression by Leveraging on Intersectionality and Citizen-Led Equity-Driven (SPLICE) Research Framework
Bibliographic record
Abstract
Intersectional theory effectively highlights the need to address the most marginalised communities through research, identifying key areas of privilege and oppression, and confronting it through praxis to advance social justice. Attempts at operationalising the theory for implementation have been diverse, but systematic frameworks to operationalise intersectionality remain sparse. This study seeks to provide a framework to operationalise the implementation of intersectionality in health equity research. Embedded within a citizen science approach, this paper suggests the use of the Socio-Scientific Progression by Leveraging on Intersectionality and Citizen-led Equity-driven (SPLICE) Research Framework, which aims to operationalise values of intersectionality into seven key principles for implementation throughout the research process: “Research Co-Creation,” “Safety For All,” “Reflexivity,” “Growth for All,” “Dynamic Ecological Context,” “Interlocking Systems of Oppression,” and a “Community-First Approach”. This paper takes reference to a collaboration research case study example investigating the use of Theatre of the Oppressed for Gay, Bisexual and Queer (GBQ) Singaporean men in 2023, to consider how this framework was developed and can be implemented in research studies. An accompanying checklist is developed to guide researchers in their implementation of the SPLICE framework.
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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 | 0.305 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.025 | 0.189 |
| Scholarly communication | 0.046 | 0.047 |
| Open science | 0.006 | 0.073 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".