A proposal for the sustainable management of natural sciences postgraduates in development: flexibility and application (Technical note)
Bibliographic record
Abstract
In Latin America, reality shows the need to improve the quality of university education of teachers and researchers, both master’s and doctoral. The graduate “Doctorate in Natural Sciences for Development” (DOCINADE) shattered the traditional higher education systems and uses the information technology and communication; the distance mediated dialogue and an integrated bimodal system to provide the Latin American community with a choice of flexible curriculum, relevant, rational and linked to its environment. Several public universities in countries in the region are already working in this graduate, for example, Costa Rica, Mexico, Nicaragua and Cuba, with the support of other institutions of higher education in the United States, Canada and Spain, among others. The program offers four emphases: sustainable crop production systems, sustainable natural resource management, environmental management and culture and applied electronic technologies. The proposal includes recognition of academic expertise and continuity in the graduate program with research topics related to local, regional, or national priorities and directing efforts toward sustainability, cleaner production and environmental education. The program has received financial support from the National Association of Universities and Institutions of Higher Education in Mexico and the Central American University Higher Council (CSUCA) during the 2001-2005 periods, the Ministry of Science and Technology (MICIT) in 2002-2011 and the National Council of Rectors (CONARE) in 2005-2013 (the latter two of Costa Rica). The graduate was designed by doctors of Latin American universities for students from the region. The objectives are to share this experience, results and find new allies and partners that will expand its coverage of action to achieve synergies and complementarities.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.023 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".