Intra‐active entanglements: What posthuman and new materialist frameworks can offer the learning sciences
Bibliographic record
Abstract
Abstract This paper examines what new materialist and posthumanist frameworks can offer learning science research in diverse maker learning environments. We explore what is gained by grappling with the entanglements between humans, non‐humans and more‐than‐humans. To do this, we draw on Karen Barad's ethico‐onto‐epistemology and agential realism where she redefines connections to the shared world by attuning to the entangled matter that is created within intra‐actions. We use this framework across four international cases: digital media camps, a university‐level classroom‐based makerspace, a Saturday outdoor makerspace workshop and a classroom‐based museum makerspace. Each case study attends to how intra‐actions enact agential forces in maker education research—forces that posthuman and new materialist frameworks help us see. In so doing, these case studies challenge many of the assumptions prevalent in the learning sciences about mattering and its implications in research sites.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.136 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".