Methods and strategies for understanding diversity, social and cultural history for elementary Science: the nature of food classification
Bibliographic record
Abstract
Food engages the senses and brings up memories and traditional knowledge. This session shares the goals, delivery and observed responses of a hands-on, interactive workshop that facilitates students’ exploration of their own senses, experiences and cultural and historical knowledge of food. Through classifying edible plants, we explore science learning skills, elucidate the importance of biodiversity and inquire into the role of science as one way of understanding the world, including Indigenous and other local approaches. Ideally, students encounter some exposure to their biases and the views of others, and may leave with full bellies. In particular, through this exercise, we encourage the skills of observation, inference, classification, and organization of information to represent knowledges. We delve into connected stories about foods from around the world, and draw upon participants’ own varied and related experiences, in a place-based pedagogical approach. The facilitators have extensive and varied expertise. One has been instructing elementary Science methods courses at post-secondary institutions in Ontario for several years while the other is an expert on field guides with a background in ethnobotany. Together we share our knowledges and experiences modelling to teacher candidates inclusive, place-based and culturally relevant science education, aimed at elementary students.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".