Toward a Poetics and Pedagogy of Sound: Students as Production Engineers in the Literature Classroom
Bibliographic record
Abstract
M discussions of successful efforts to engage students in multi-modal discourses and prepare them for adapting to digital formats have focused on composition and creative writing classrooms. Cynthia Selfe, Lev Manovich and others have called for aural, visual, and other multi-modal approaches not only because of diverse learning styles and ever-changing technologies of communication, but also because these modes are important to different communities and cultures (Selfe 616). In literature classes, even if we use multi-modal assignments, the focus on writing critical analysis though a creative practice may seem more distanced from the generative aspects of “making” in a composition or creative writing classroom. This distinction, with its blurry edges, echoes the debate among digital humanities theorists between theorizing and making. I would argue that literature classrooms in the 21st Century are spaces ripe for exploring multi-modal experiences that mix up the critical and the creative, theorizing and “making.” Literature classrooms can incorporate more of what Amanda Stirling Gould calls a “makerspace learning environment” (26) so that we not only think about, but “think with” the media we use (Hayles, How We Think 24). Leading digital humanities scholars contend that [t]he social, political, and ecological challenges of the 21st century demand significantly more than textual analysis or recitations of inherited content. These problems (and opportunities) will need people trained to create synthetic responses, rich with meaning and purpose, and capable of communicating in a range of appropriate media, including but not limited to print. (Burdick, Drucker, Lunenfeld, Presner, and Schnapp 25)
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".