Arts-Based Engagement Ethnography: An Approach for Making Research Engaging and Knowledge Transferable When Working With Harder-to-Reach Communities
Bibliographic record
Abstract
In social science research, epistemological assumptions regarding what constitutes valid research fall into two main areas of inquiry—qualitative and quantitative. Within a qualitative paradigm, eliciting a close and often intimate exploration of phenomenon from a text-based or verbal approach is privileged, and in a quantitative paradigm, obtaining a systematic, large population survey or questionnaire approach is prioritized. Although the two are not mutually exclusive, with the development of each, the visual and the kinesthetic aspects have both largely been lost. This article proposes an arts-based engagement ethnography (ABEE) as a means of reclaiming these visual and kinesthetic aspects in order to engage in culturally sensitive research with underrepresented communities. To this end, this article outlines some of the limitations of current research and explores how cultural probes (a set of simple items given to participants to help them document their experiences) can be used to enter qualitative research from a different epistemological vantage point. Moreover, this article discusses the use of qualitative interviews and focus groups in ABEE and the manner in which this methodology allows for unique knowledge mobilization possibilities. It highlights how these are built into the research design, and how this is an important part of the approach's ability to engage harder-to-reach communities.
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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 | 0.052 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".