Theory Follows from Practice: Lessons from the Field
Bibliographic record
Abstract
Theory is a distillation that makes the inevitable trial and error of teaching and learning count for lessons to be passed on. We learned this from working on engaged humanities projects, and also from reading Frederich Schiller, John Dewey, and others who share a fascination with doing as a trigger for feeling and thinking. The sometimes humble practice of service learning generates lessons that add up to the broad intellectual ambition of public humanities: to explore and expand the civic dimensions of art and interpretation, and to demonstrate through practise the centrality of the humanities to democratic processes and social justice. Theory not only informs engaged humanities practices: theory is transformed by these practices, as are those who participate in them. At a time when the value of the humanities is continually in question, it is all the more essential to engage the humanities in the dialogical process of refashioning education, community, theory, and the humanities themselves.
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.147 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".