Bibliographic record
Abstract
Introduction: When we conducted a qualitative study about nature with German and Canadian children in 2014, we began the community of inquiry with seemingly simple questions that engaged with the children's sensations, feelings and associations.1 We asked: What is the first thing that comes to your mind, when you think of nature? And as a follow-up question: Can one smell, taste, hear, see nature? If so, how? Here is how one group of children responded: Nate, 15 years: one can, switch off one's brain when I think about nature, for example, when sitting on the lake, to feel the nature, for one hour to not think about all the terrible things, all the problems of mankind. Liza, 12 years: So, I really enjoy nature, for example when I go for a walk with my parents and listen to the birds singing, this I really enjoy. Rick, 11 years: Most of the time peace and sometimes like shock. [...] Patrick, 10 years: So, I can imagine that one can be excited; [... for example] lightening is nice and bright and beautiful; that is to say when one looks at it from somewhere safe. But when you are not somewhere safe then it is not only beautiful ... I mean, it is still fascinating, but when you are standing there in open nature, then you are also afraid, I think.2
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.066 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".