Implementation and Outcomes of Outdoor Science Education in an Urban Setting on Primary and Intermediate Level Students with Emotional and Behavioral Disorders
Bibliographic record
Abstract
The term nature deficit disorder describes the “human costs of alienation from nature” (Louv, 2019). While not meant to be a medical diagnosis, Louv argues that the condition has, “profound implications, not only for the health of future generations but for the health of the earth itself” (Louv, 2008). Children most at risk are those who live or go to school in an urban setting, as well as students diagnosed with Emotional and Behavioral Disorders (EBD). Students diagnosed with EBD are often kept indoors as their teachers and caretakers are frequently trained in the indoor use only of behavior management techniques (Riden et al., 2022). This means that during professional development training, any that pertain to behavior management techniques, procedure, or protocol are routinely taught inside of a classroom, conference room, or recently home office, using an indoor scenario (classroom, auditorium, cafeteria, etc.) as an example of when and how to use these behavior management tools. Outdoor professional development, equipping students with natural tools that can help improve not only their physical but also their mental health, as well as connecting students with nature in a way that teaches them to advocate for the health of the earth, thus becoming citizen stewards are all themes that are part of the massive, currently dysfunctional system that is outdoor education in schools. This study shows that when students are encouraged to interact with nature on their own terms on-task behaviors and motivation increase. Students also retained lesson information and asked follow-up questions after the lesson when taught outside using hands-on activities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".