Student research collaboration as conservation education: A case study from the primate field school at Maderas Rainforest Conservancy
Bibliographic record
Abstract
Maderas Rainforest Conservancy (MRC) is a conservation-focused non-profit organization that is devoted to protecting the tropical forests they manage in Costa Rica and Nicaragua and to providing conservation education for international university students through biological field schools. The MRC Primate Behavior and Ecology course is their most frequent course offering and is aimed at developing students to be independent field researchers. This course involves classroom lectures, training in primate identification and field methods, and the execution of independent research projects that students design, collect data for, and write up as scientific papers. Student development as conservationists is facilitated through the research experience provided by this field course as well as through co- and extracurricular research opportunities available to students at the sites that MRC manages: La Suerte Biological Research Station in Costa Rica, and Ometepe Biological Research Station in Nicaragua. In tandem with their participation in the MRC primate field course, we (Laura M. Bolt and Amy L. Schreier) consistently offer students research opportunities in our ongoing project examining the impact of forest fragmentation on primate behavioral ecology. MRC student course evaluations indicate that this co- and extracurricular research participation substantially contributes to student academic development and conservation awareness. Student research collaboration, therefore, greatly benefits students as well as furthers MRC's conservation goals as a non-profit organization. In future MRC primate field school sessions, we will continue to offer research collaboration opportunities to students and will also endeavor to improve conservation education at MRC by involving more local community members in MRC's academic programs.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.045 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".