10. Teaching World Literature for the 21st Century: Online Resources and Interactive Approaches
Bibliographic record
Abstract
This paper introduces a pedagogical approach and strategies for using online resources and interactive media to teach in English about writers and writing from around the world without colonizing or excluding other languages and cultures. First, I explain the context and challenges of teaching world literature: the importance of including diverse works and authors; competing definitions and information overload; barriers to international availability and accessibility of non-dominant works and of non-English languages; and students’ limited historical and cross-cultural knowledge. I then show how faculty can incorporate open-source Internet and interactive multimedia resources into world literature courses in ways that allow alternative and outsider voices to challenge and expand national, ethnic, linguistic, socio-political, and disciplinary boundaries. Faculty can use web resources such as blogs and wikis; online self-publishing and translation sites; maps, timelines, primary documents, and other sources of historical and socio-political context; and social media platforms to create a more inclusive notion of world literature, as well as a learning process that is dynamic, collaborative, and relevant to students’ daily lives.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".