Bibliographic record
Abstract
Since 2007, the Humanities for Humanity (“H4H”) course has brought together student experience beyond the classroom, educational experiences for community members who could not otherwise attend university, discussion of social justice, and studies in the humanities. By discussing a selection of rich and influential primary texts from the humanities, course members are introduced to a rudimentary history of the present, focussing on who we have become as members of a concrete social and political reality intersected by capitalism, bureaucracy, liberalism, socialism, anti-essentialism, and post-colonialism. Both the texts and the student-participant encounters are rich, and the sessions are guided by two central classical ideals: the activity of learning is primarily an end in itself, and the most important thing to learn may be who we are. The core course content of H4H is outlined, and the ways in which H4H connects student mentors and community participants are discussed. Implications are drawn regarding what makes H4H a unique form of community service-learning in which service is virtually eclipsed by learning in a process that subverts barriers between people.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.263 | 0.100 |
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".