Drawing Gender Equality: A Participatory Action Research Project with Educators in Northern Uganda
Bibliographic record
Abstract
This paper reports upon an arts-based participatory action research project conducted with a cohort of 30 teachers in rural Northwest Uganda during a one-week professional development course. Multimodality (Kress & Jewitt, 2003; Kress & van Leeuwen, 2001) was employed as a “domain of inquiry” (Kress, 2011) for social semiotics (meaning-making within a social context) within which the participants both represented gender inequality as well as imagined gender equality. Multimodality recognizes the vast communicative potential of the human body and values multiple materials resources (such as images, sounds, and gestures) as “organized sets of semiotic resources for meaningmaking” (Jewitt, 2008, p. 246). Providing individuals with communicative modes other than just spoken and written language offers opportunities to include voices that are often not heard in formal contexts dominated by particular kinds of language, as well as opportunities to consider topics of inquiry from different perspectives and imagine alternative futures (Kendrick & Jones, 2008). Findings from this study show how a multimodal approach to communication, using drawing in addition to spoken and written language, established a democratic space of communication. The sharing and building of knowledge between the participants (educators in local contexts) and facilitator (university instructor/researcher) reflected a foundational tenet of engaged scholarship which requires “…not only communication to public audiences, but also collaboration with communities in the production of knowledge” (Barker, 2004, p. 126).
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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 | 0.037 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".