Bibliographic record
Abstract
This article focuses on teaching science through Vision I, Vision II and Vision III which is an increasingly important but understudied aspect of science literacy. Dr. Poh Tan and Eduardo Gluck, interviewed Clarah Menezes, an elementary school teacher in Novo Hamburgo in Southern Brazil. Clarah teaches Grades 4-5 within a marginalized community and most of her students have varied levels of literacy and numeracy. Dr. Tan visited Clarah when she went to Brazil in 2018 as a visting scholar. Dr. Tan and Clarah have been working together for the past year on disrupting traditional approaches to teaching science. Clarah’s new approach to teaching science is built on Dr. Tan’s framework that builds upon Roberts and Bybee’s attributes of a scientifically literate person. Dr. Tan’s framework includes a perspective of teaching science from a relational and more than human connection with entities, including animals, nature and material. In this interview, Clarah shares her experiences, struggles and insights into teaching science by applying the three-vision framework.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".