Bibliographic record
Abstract
The study ‘New Media in the Humanities: from metaphors of inevitability to metaphors of possibility,’ argues that using digital technologies in humanities classrooms (at the post-secondary level) is transformative for both students and professors. It begins by identifying and then allaying the fears that scholars in the humanities harbour: the computer reduces literacy, diminishes knowledge to mere information, annihilates the metaphysical in the academy, and disconnects the student from his/her humanity. The second section of the article outlines in detail the exciting possibilities of engaging electronic media in the classroom, which include moving beyond a single literacy to multiple ones (post-/polyliteracy), recognizing digital technologies as potential cognitive systems parallel to our own (post-humanity), evolving from notions of a single subjectivity to global interconnectedness (post-identity/ post-nation), transcending one's chosen discipline in order to discover new interdisciplines via the Web (post-/transdiscipline), and exploding the confines of print in order to discover new e-discourses (post-symbolic). The study also provides case studies of Canadian and international scholars in the humanities who are putting these novel ideas into practice in the classroom.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.067 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".