Bibliographic record
Abstract
This workshop explores how teaching within the humanities is affected, challenged, or improved by public outreach. This question arises from growing concerns that the humanities has become disconnected from the general public or incoherent outside of a post-secondary institutional setting. Research suggests that in addition to the demands of their own research, scholars in the humanities are facing the added pressures of “policy-makers [who] are increasingly demanding that academics justify themselves in terms of the returns that result from investing in their scholarly domains” (Benneworth, 2015, p. 4). These concerns inform the public humanities movement which seeks to foster relations between scholars and their local communities in an effort to champion civic engagement/learning and accessible scholarship. The goal of this workshop is to consider in detail how teaching practices in the humanities might benefit from such community outreach, as well as to support and offer resources to instructors looking for new ways to engage students in this way. The workshop introduces participants to the public humanities movement, initiates debate on the relationship between humanities teaching and public outreach, and suggests strategies and resources for instructors to connect with local communities and outreach programs.
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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".