Bibliographic record
Abstract
Workers today are faced with possibilities of wider networks of knowledge generation. Learning in and through work is one of the many spaces in which pedagogy may unfold. Web technologies amplify this fluidity and networked learning now encompasses a plethora of practices. New technologies are believed to contribute to more mobile and connected professional learning and knowing practices. Yet, objects do not act by themselves, and it is the relations around these technologies—the sociomateriality of the configurations assembled—which potentially reconfigure ways of knowing. In this paper, the negotiation of relational and material aspects of online pedagogical practices is explored. I focus on the delete button and deleting practices of self-employed workers engaged in informal work-related learning in online communities. Exploring a pervasive everyday practice, such as deleting, affords glimpses into the sociomaterial entanglements energizing enactments of online pedagogy and knowledge production. Understanding the delete button as a fluid object in fluid space begins to illuminate its complexity. Deleting practices which work to stem the tide of information pushing itself onto screens, as well as those practices that attempt to delete traces left behind on screens and “in the cloud”, are examined. Constantly negotiating absence and presence, deleting practices mobilize both digital inclusion and exclusion. Such sociomaterial practices around the delete button shape interactions with information and knowing possibilities and enact networked learning practices in particular ways. Although disarmingly straightforward at first glance, by unravelling some of the complex human-object assemblages associated with deleting, opportunities for interruption and innovation in online learning practices emerge. Actor Network Theory (ANT) provides the theoretical and conceptual tools for this exploration. ANT is well suited for studying complex and mobile practices which take the pervasive role and energy of objects into account. Emphasizing more critical understandings of the co-constitutive and performative relationship between people and web technologies, and how these relations both smooth and complicate work-learning practices online, enables adult educators to keep Latour’s (2005) “matters of concern” open. 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".