Culture, language, knowledge about nature and naturally occurring events, and science literacy for all: She says, he says, they say
Bibliographic record
Abstract
Pauline Chinn and Brian Hand, both well-established science educators interested in the role that language plays in doing and learning science but with distinctly different stances, provide a glimpse into an ongoing conversation and deliberation over 18 months about what does it mean to come to know in science and how does this concept of science translate into pedagogical practices. Larry Yore moderates and promotes these conversations and deliberations to help identify intersections and shared understandings and to contrast areas of differences and disagreements. These professional reflections on their critical thinking and fundamental assumptions about culture, language, and knowledge about nature and naturally occurring events demonstrate the necessary and essential processes required to move the science literacy for all agenda forward. They share their fundamental stances and perspective about sociopolitical issues, postcolonial stances, science, and schooling without being sidetracked from their purpose to inform and increase awareness about the critical issues in science literacy for all. Their conversations and insights may well be equally informative and empowering to students from majority and minority cultures, since all learners appear to be second language learners when it comes to science language, linguistic devices, and discourse patterns.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".