Ecocentric Friluftsliv in a Pedagogical Context: A Practical Approach to Ecophilosophy
Bibliographic record
Abstract
The ideas in ecophilosophy might appear as abstract concepts unless concretised. To remedy this, we suggest a practical approach to teaching ecophilosophy through the Scandinavian outdoor education tradition of friluftsliv. Our aim is to investigate how an outdoor education project concerning making firewood, from forest to fireplace or campfire, can convey concepts from predominantly the Norwegian ecophilosopher Sigmund Kvaløy Setreng to adult friluftsliv students. Our theoretical foundation shows that the ecocentric and pedagogical perspectives in friluftsliv have corresponding ideas to ecophilosophy in general. And a closer look at Setreng’s work in the field of ecophilosophy, with concepts such as “complexity versus complication,” “meaningful work,” and “organic time,” reveals practical dimensions applicable to friluftsliv. These dimensions are incorporated in the firewood project in an attempt to convey ecophilosophy to the students. Based on a description of the firewood project from the professor’s perspective, we analyse and explore in what way the experiential learning activities in this project convey selected ecophilosophical concepts. Our conclusion is that the firewood project to some extent indeed communicates important aspects. We thus argue that a practical approach like the one found in the firewood project can contribute to spreading the ideas of ecophilosophy.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".