Navigating Approaches to “Thinking With”: A Discussion of the Practicalities of Posthuman Research Involving Young Children
Bibliographic record
Abstract
Abstract Though there has been a marked increase in research driven by posthumanist theory and inspired by the common worlds research approach, practical approaches to conducting this type of research have not been well documented and shared within the literature. This article explores the process of navigating the planning and conducting of research that aims to think with more-than-human worlds. Three research methods that were applied in a study involving young children in a forest school program are described: (1) non-participant observation, (2) observing the park through “sit spots,” and (3) the use of wearable cameras to film a different perspective. I explore each of these as a way to guide other researchers grappling with the tensions and challenges of conducting posthumanist research. Any combination of these methods could be considered within research that aims to disrupt the dominant anthropocentric lens in early childhood education for sustainability and beyond.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".