Bibliographic record
Abstract
As the developers of Journey to El Yunque, we have taken a different approach to the process of designing a science curriculum. Rather than start with a specific set of concepts or skills to target as learning outcomes, we started by identifying a specific community of practice to which we sought to connect students. Researchers in the El Yunque rainforest in Puerto Rico have been studying the impact of hurricanes on ecosystem dynamics and have been modeling what the long-term impact would be if changes to the global climate increase the frequency of severe hurricanes. Therefore, hurricane impact became the focal phenomenon for the unit. We modeled the process of investigating hurricane impact after the long-term ecological research practices of researchers in El Yunque. Students begin by investigating the long-term impact of hurricanes on the producers in El Yunque. Next students investigate the long-term impact of hurricanes on various consumers in the rainforest. Finally, students investigate how hurricanes impact the cycling of resources directly as well as indirectly through changes in organisms’ use of those resources in the rainforest. A central tension in the design process is how to coherently represent the spatial relationships between the components of the ecosystem and the temporal dynamics of the individual components. In this paper, we present the evolution of the program as we sought to balance that design tension and build an environment that connects students to the central phenomenon and practices of the community of researchers in El Yunque.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".