Reclaiming and re-embodying experiential learning through complexity science
Bibliographic record
Abstract
At a time when ‘informal’ and ‘practice-based’ learning are receiving unprecedented emphasis in lifelong learning debates, this article offers an apologia for experiential learning (EL) in adult education. Taking the position that the signifier of experience allows a foregrounding of the problematics of experience and the centrality of embodiment in learning, the argument does not deny theoretical weaknesses plaguing the experiential learning discourse. In fact, four problems are described in EL theory and practice: ontological splits that ‘lose’ the body; disciplines that control the body; educational management that schools experience; and resulting exclusions. Towards reclaiming a more productive discourse of EL, an argument is presented for conceptually ‘re-embodying’ EL, drawing from complexity science. Three themes of re-embodiment (co-emergence, desire, and struggle) are presented. Pedagogic practices and reconfigured roles for adult educators suggested by these themes are discussed in the final section.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.065 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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 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".