Experimenting with Diffractive Analysis Practices While Walking-with River: Audiowalking and Micromapping
Bibliographic record
Abstract
Abstract This paper shares how a river-walking project in early childhood education created and experimented with two practices diffractively as an effort to do research differently. The year-long study, situated in Western Australia, explored river-child relations while walking with Derbarl Yerrigan/Swan River and was interested in decentring the human and attuning to more-than-human relations through situated practices. Using a feminist environmental framework this project took a non-representational approach to analysing data through two intra-related diffractive concepts: re-turning and re-membering. These concepts grounded the two practices, audiowalking and micromapping, and helped to shape the various forms of experimentation for a diffractive approach to analysis. Audiowalking is a practice that involved creating narrated audio recordings while walking with an intention of layering data from the present with pasts and futures. Micromapping is an embodied and performative practice that reimagined and unsettled place and space through mapping emotional encounters, river relations and the more-than-human. This paper shows how environmental education researchers, particularly those conducting place-based research, can approach research analysis diffractively to disrupt colonial ways of knowing, being and doing research through two practices that take a non-linear conceptualisation of time, embody data and research with worlds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".