Development of a Methodology for Educational Management Entailing Government, Economic Sectors, and Educational Institutions for Sustainable Development
Bibliographic record
Abstract
Worldwide guidelines since the 1980s have developed mechanisms with national and international economic funds for scientific and technological development that reduce greenhouse gas emissions (GHG) and mitigate environmental impacts. The objective of this chapter is to analyze the political, socioeconomic, and regulatory characteristics of Mexico as a country with a developing economy, as well as the actions that have made the country go backward in the energy transition, to make a proposal that serves as a guide. Among the precedents have been the Montreal Protocol and the Kyoto Protocol, which have sought to mitigate the use of greenhouse gases in industrialized countries and involve developing countries through various mechanisms that encourage the reduction of GHG, such as carbon credits. The UN (United Nations) continues to work with cutting-edge initiatives and programs such as the sustainable development goals (SDGs).
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".