Scientific communities of practice: <scp>K–12</scp> outreach model around organism responses to repeated hurricane disturbances
Bibliographic record
Abstract
Abstract Collaboration between ecologists and learning scientists can give rise to powerful models for scientific outreach within ecology. This paper presents a process by which learning scientists and ecologists codesigned a science curriculum that invites students to join an ecological community of practice. In the Journey to El Yunque middle school science curriculum, students engage with simulation models generated from data gathered by Luquillo Long Term Ecological Research (LUQ LTER) scientists. Journey to El Yunque students can explore post‐hurricane population changes in yagrumo (Cecropia schreberiana), tabonuco (Dacryodes excelsa), coquís (Eleutherodactylus coquí), snails (Caracolus caracola), anoles (Anolis stratulus and A. gundlachi), veiled stinkhorn mushrooms (Dictyophora indusiata), and caterpillars (Historis odius). Ecology‐based revisions to Journey to El Yunque have included adding models of the effects of repeated hurricanes on limiting factors, based in part on findings from a canopy trimming experiment. Revisions based on classroom testing include simplifying student‐facing model controls to allow students to focus on the essential model components. The ongoing collaboration that keeps the Journey to El Yunque curriculum on the cutting edge of ecological and educational advances has been sustained for over two decades. We attribute the longevity of this work to (1) the long‐term nature of LUQ LTER, (2) a sustained interdisciplinary collaboration, and (3) our long‐term relationships with schools.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".