In Between Familiar-Unfamiliar: Research Travel as Arts-Based Research
Bibliographic record
Abstract
Artists, researchers and teachers often find their work positioned in-between the familiar and the unfamiliar. The arts linger in-between such familiar-unfamiliar events, objects and places, unfolding new understandings and potentialities by making the familiar strange and the unknown familiar. Through a collaborative arts-based educational research project conducted in Japan, we address the following question: How can artistic practices lend to pedagogical possibilities when we attend to new things in familiar ways, and when we situate familiar things in new ways? Through a combination of a/r/tography and walking method, we engage in a series of walks, conversations and creative practices that explore layers of relationality through research travel. Walking as an arts-based research practice emphasizes the physicality of our nature as an embodied being grounded in movement; “walking is not just what a body does; it is what a body is” (Ingold & Vergunst, 2008, p. 1). Working together from different perspectives troubles a binary understanding of the insider-outsider relationship, focusing instead on the “-“ as a site of a hyphenated positionality. The walking sessions form a relational correspondence with each other that reveal rhythms of our walks, relationships and experiences while we attune ourselves to ways of lingering in-between familiar and unfamiliar places. Through the embodied and metaphorical walking in-between the familiar-unfamiliar, we consider the pedagogical implications of research travel as site of collaborative arts-based inquiry and what further questions they may raise.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".