“A podcast would be fun!”: The fetishization of digital writing projects
Bibliographic record
Abstract
While digital writing projects (DWPs) like podcasts, videos, and infographics are rigorous sites of scholarly knowledge production, the growth in their popularity as classroom assignments often has more to do with a sense that these are “fun” assignments. Horner, Selfe, and Lockridge (2015) describe such dismissive attitudes using the term fetishization. When DWPs are fetishized by students and faculty, they are celebrated while being dismissed as pedestrian fads. Ultimately, fetishization decreases the amount of support offered by faculty, the effort invested by students, as well as the demand (and budget) for learning service support. This means that disparities between students (including access to technologies, digital literacies, and “normative” abilities) are exaggerated. In this paper, we illuminate four interconnected drivers of fetishization that obscure the realities of DWPs—the myth of digital natives, assumptions about tool-content division, faith in digital tool neutrality, and idealizations of the web. Like all teaching approaches, thoughtful instructional design and learning supports are required for DWPs to create effective, equitable, safe, inclusive, and accessible learning opportunities. This paper enhances writing instructors’ and tutors’ ability to challenge fetishized perspectives of DWPs in their work with faculty and students alike.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".