The journal’s the thing: teaching natural history and nature writing in Baja California Sur
Bibliographic record
Abstract
The skills of making informed observations, synthesizing those observations, and communicating them effectively are central to the naturalist. Developing university courses that optimize instruction in these skills simultaneously can, however, be a challenge. Here we describe a program at Santa Clara University comprised of two integrated co-requisite courses, Writing Natural History (ENVS 142) and The Natural History of Baja (BIOL/ENVS 144). Lectures through the 10-week winter quarter expand students’ knowledge of the ecosystems and biodiversity of the Baja Peninsula and help them to develop descriptive writing skills. The courses culminate in a ten-day expedition to the Baja Peninsula and Isla Espiritu Santo in the Sea of Cortez, where students explore local ecosystems and journal about their experiences. The result is a program in which students expand their skills in natural history and develop their own voices as writers and natural historians. We describe the structure and philosophy of this program and provide details on associated lecture topics, logistics, exercises, and readings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".