Arts-Based Approaches to Studying Traveller Children’s Educational Experiences
Bibliographic record
Abstract
In this article, we present ideas on how arts-based methods can be applied to conducting research with a minority ethnic group (i.e., Traveller children) and offer ways to analyse data. We refer to the culture of Traveller children, report statistics on their educational performance and refer to recent research in Northern Ireland on their disengagement from compulsory post-primary (11-16 years old) education. We look through the lens of Bronfenbrenner’s ecological systems theory and consider a re-think of the approach typically used in research to tap into Traveller children’s educational experiences. We offer a brief summary of the principles of arts-based research, outlining the theoretical underpinnings of supporters who argue for its use in educational research settings. We elaborate on three arts-based research methods as options in the design of conducting research with Traveller children and offer advice on associated ethical issues. In exploring methods of analysis, we refer to the types of data and suggest a content and thematic analytical approach to interpret the data. In conclusion, we reiterate the importance of offering these culturally responsive means to engage with this minority ethnic group.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".