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 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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".