Introducing Interdisciplinary Curricula Into Conservation Biology: Exploring Changes in Students’ Perceived Proenvironmental Attitudes and Behaviors
Bibliographic record
Abstract
Today, conserving the natural environment is paramount. Educators have been striving to develop pedagogical approaches that facilitate greater engagement in conservation behaviors. However, many of these reforms have been targeted at an institutional level, without necessarily testing whether changes in proenvironmental perceptions, attitudes, or behaviors occur for students. This step seems important when developing conservation biology courses that provide well-rounded education that may better prepare students for future challenges in biodiverse conservation contexts. Our objective was to assess the proenvironmental attitudes and conservation values of undergraduate students enrolled in an undergraduate conservation biology course before and after instruction to determine whether a multidisciplinary curriculum, in conjunction with traditional conservation biology content, would alter their perceptions. Students in both the control and intervention groups felt relatively neutral about a range of environmental and conservation topics. No statistical significance between curricula and impact on student perception was revealed. However, in the experimental course, shifts were found concerning students’ understanding of the complexity of conservation. Results also highlight long-standing issues related to conservation education, such as a bias toward mammal conservation, and suggest that guest lectures are insufficient to bring about attitude change related to sustainability. Further research on incorporating cross-disciplinary pedagogy into STEM courses is recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".