Bibliographic record
Abstract
As a science teacher I often tried to "shift" the viewing of the world by my middle-school students from being very human-centric towards one that was more focused on the world as experienced by the organisms in the (eco)systems we were studying, to think about the world from the "level" of the organisms and the richness of the lived experiences they were having. One approach I used was showing them scenes from the movie "Honey I Shrunk the Kids" where the world view of the human participants was considerably shrunken to that where they could "ride" on the back of a bee and experience simple rainfall as a small organism would. I used Barbara McClintock's description of her thinking like corn, trying to imagine what it would be like to be corn (see Keller, 1983; Henry, 1997), as a foundation for my thinking on this as a teacher, and engaged my students in science talks (Gallas, 1995) to get them to start thinking about the world in more complex ways, in some senses as experienced by other organisms and from a different point-of-view, a different perspective and scale, than humans doing science usually start from. – GMB
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".