Way Markers in the Practice of Shambling: A Method for Communal Discernment
Bibliographic record
Abstract
This article traces the development of a place-based approach that contributes to exploration of post-qualitative methods. Working with the question, how can our understanding of embodied learning lead to habits of embodied inquiry that shift our relation to what socio-ecological justice means in this time? we embarked on a writing/making/sensing time in a series of different academic workspaces. The reflections and poems recount our developing awareness of what it would mean to turn away from exploitative terms of exploration and toward ones guided by Indigenous wisdom, not just as a rhetorical flourish, but as everyday embodied keeping faith with Indigenous principles through and beyond all research phases. What we present here is not a new set of skills but a shift in orientation that brings the weight of our experiences, knowledges, and personhoods to the fore, and asks us to locate and presence ourselves in and with the breath of the world. We note that this practice shifts the quality and relationality of storying that emerge from the practice and the role embodied knowledge plays within them. We conclude with reflections on the application of this practice to the different phases of research.
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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 | 0.030 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".