The role of triangulation in sensitive art-based research with children
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight the critical role of triangulation to create authentic analytical frameworks amidst contradictory participant narratives in sensitive art-based research (ABR) with children. Design/methodology/approach Multiple qualitative case study research included three months of participant observation, individual semi-structured teacher interviews and open-ended art-based interviews using the Draw-Write-Narrate method (Ogina and Nieuwenhuis, 2010) with upper primary students in two schools in Kirinyaga County, Kenya. Findings The art-based approach to student interviews, combined with participant observation and teacher interviews, provided a child-centred process that illuminated students’ understandings and experiences while minimizing risks to participants. Its application requires researchers to recognize data collection and analysis as subjective processes that strongly benefit from triangulation to interpret a diversity of perspectives that may not easily align. Originality/value As ABR with children increases in popularity, it is important to identify challenges in the process of analysis and meaning-making. This paper identifies triangulation as a valuable tool for handling the challenge of diverse perspectives from child participants, particularly in conducting sensitive research that may increase the likelihood of contradictory narratives.
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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 | 0.438 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.022 | 0.075 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.007 | 0.008 |
| 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".