I’m deleting as fast as I can: negotiating learning practices in cyberspace
Bibliographic record
Abstract
Learning in and through work is one of the many spaces in which pedagogy may unfold. Web technologies amplify this fluidity and online learning now encompasses a plethora of practices. In this paper I focus on the delete button and deleting practices of self-employed workers engaged in informal work-related learning in online communities. How the relational and material aspects of online pedagogical practices are being negotiated is explored. While deleting appears to be an everyday practice, understanding the delete button as a fluid object in fluid space begins to illuminate its complexity and multiple enactments. Deleting practices which work to stem the tide of information pushing itself on to screens, as well as those practices that attempt to delete traces left behind on screens and ‘in the cloud’, are examined. Actor-network theory provides the theoretical and conceptual tools for this exploration. I conclude with observations on the politics of the delete button and implications for more sophisticated digital fluency in everyday pedagogy.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".