Wilding liability in education: Introducing the concept of wide risk as counterpoint to narrow-risk-driven educative practice
Bibliographic record
Abstract
Consideration of risk and liability in outdoor educative practice has normally been limited to the narrow risks, usually to physical health, of incidents that can cause a particular injury. In this view of risk management, the more readily controlled the circumstance, the less likelihood of risk and consequent liability. Thus, to reduce risk, learning in the natural world is often avoided because it occurs in far more complex and less controllable contexts than human-created ones. However, wider and more grave risks to physical, emotional and mental health that may accrue through a life that is lived in separation from the natural world are not often considered or evaluated. In part, this may be because these kinds of risks are less immediately evident, and liability for negative outcomes may be more difficult to measure. Thus, there is less incentive to consider them. However, delayed outcomes are still outcomes. To consider easily discerned narrow risk alone, while ignoring more complex and longer-term wide risk, is no excuse for avoiding the ethical responsibility that public education carries to provide both the safest and most fecund context for learning. This paper introduces the concept of wide risk as a counterpoint to the narrow risk calculations now performed, and argues that in incorporating an understanding of wide risk in educative practice, at least two results are likely. The first is that learning outdoors will frequently be discovered to be a less risky alternative, if a broad range of outcomes over time are considered. The second is that the value of embracing risk in all aspects of learning ought to become a part of the learning process, and part of what is taught in public schools.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.004 | 0.071 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".