Socioenvironmental Management, the Denial of Theory U and the Amazon Rainforest Fires
Bibliographic record
Abstract
The uncontrolled use of fire has resulted in innumerable occurrences of forest fires in the Western Amazon, causing damage to the environment and society, verifying its association with the effects of corruption, since it deals with the selfish behaviors of some involved actors, aiming the well-being of a minority. This study is a case study that focuses on forest fires and their relationship with corrupt practices and Theory U. It brings as its objective general study of the relationship of corruption to forest fires in the Western Amazon; and have as specific objectives to raise the causal relationship of corruption in face of the denial of Theory U (1), to characterize the factors that involve the questions of the fires in the Western Amazon (2), and to offer efficient subsidies to impact the fires considering the attitudinal convergence of the Amazon (3). As a result, there is a denial of Theory U in the face of the selfish conduct of individuals who cause forest fires, since they are limited to the imprisonment of satisfaction of isolated wills, without seeking to emerge a future with greater social inclusion. The subsidies pointed out in this study allow us to verify the need for effectiveness in the inspection and control actions regarding forest fires by public entities related to the environment. It is up to civil society since everyone is harmed in this process, to self-organize and demand more effective measures from environmental managers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".