Exploring scientific complexity with authenticity and inclusion: New curricula and assessment materials for phytobiome STEAM kits
Bibliographic record
Abstract
Abstract It has been noted we are in a post‐truth era and unfortunately misinformation exists even in science. Furthermore, polarization of society is negatively impacting debate. Critical thinking, systems thinking, and thoughtful creative innovation can help combat such negative pressures on science. A phytobiome approach involves studying a plant as its own ecosystem or biome (plant, its associated organisms, and its environment). It also has complexity, including multiple hypothesis testing approaches, that can be missing in science literacy programming. We developed a new curriculum for phytobiome science, technology, engineering, arts, and mathematics (STEAM) kits. Our four phytobiome STEAM kits (preschool to grade 3, grade 4–7, grade 8–12, and adult) teach a phytobiome thinking approach using creative methods such as poetry, drawing, and so on, specifically in (i) hypothesis generation, (ii) study design, (iii) complexity in science, and (iv) STEAM reflection. We also use newly created (in another publication) plant, environments, associated organisms, and interactions model tables. To our knowledge, this is the first time phytobiome science literacy material has been created for such a wide range of ages and with incorporating the arts to help with inclusion and engagement, complexity, creativity, and critical thinking promotion. Our authorship team consists of students (high school and undergraduate), educators, and scientists, which in itself is a useful contribution, as we co‐created the kits. The curriculum material and other information in this article can foster future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".