Toward a Poetics and Pedagogy of Sound: Students as Production Engineers in the Literature Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it