Contributos do lazer desportivo outdoor na criação de novos campos de pensar e agir
Bibliographic record
Abstract
The development of outdoor activities is a topic that has been discussed by many sectors of society over the years. These activities can be important catalysts for the social development and recovery of regions weakened from the social, economic, demographic and environmental point of view. This study aims to establish the contributions brought by outdoor activities to the creation of new ways of thinking and acting, based on the analysis of social and economic benefits, for practitioners and for the environment. The research was an exploratory study in a qualitative approach that analyzed information from interviews, documents and observations. The documents consisted of guidelines, programs and plans on the geographical and socio-environmental characteristics of the practice sites in the city of Porto Alegre, Brazil. The interviews, in a total of 29, were held with representatives of government, sports federations, NGOs, lecturers of higher education institutions, entrepreneurs and outdoor activities instructors. The observations, 8 altogether, occurred in regions with geographical features that allow the practice of such activities. The results showed that the contributions of outdoor activities are important catalysts for new thinking and acting perspectives, having a social, economic and environmental positive impact in the regions where these activities are developed in a sustainable way.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".