Bibliographic record
Abstract
When adults are asked to think of something they love to do, something they have learned about over an extended period of time, something that gives them pleasure (though not without struggle), nearly everyone will identify a pursuit that is connected with the arts, the body, or the natural world.For many years, I have pondered what it is about these three areas that are linked, and why it is that pursuits in the arts, pursuits involving learning through one's body, and pursuits that involve intimate interactions with the natural world, are so important to our out-of-school learning.I have come to believe that what composing music, playing soccer, and hiking in the Rocky Mountains have in common is that they have the potential to fully engage the intellect, the emotions, the spirit, and the physical being.Further, it is these kinds of activities that connect us, in some fundamental way, with the earth and with what it means to be human on this spinning blue ball.I would make the claim that the passion and seriousness with which people engage in such pursuits is precisely the passion that is required to nurture and sustain meaningful learning in schools.This kind of learning is possible in all subjects.Mathematicians and scientists who are at the top of their form-whether they are six or sixty years old-experience the same exhilaration from their creations and discoveries as do artists and dancers.Mathematicians talk about the beauty of proofs, the elegance of solutions-judgments that involve an
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.048 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".