Genre, knowledge and digital code in web-based communities: an integrated theoretical framework for shaping digital discursive spaces
Bibliographic record
Abstract
Emerging digital discursive spaces, such as wikis, offer new opportunities for knowledge communication. However, participants join such spaces through the lenses of their established discursive practices. These practices, however, interact with the code – the technological design – of these spaces, which can reproduce, question, or undermine them, and present alternative opportunities and visions for knowledge communication. Participants, therefore, ultimately face questions about the ways in which tensions between established (genred) practices and alternative practices enabled by code are to be negotiated. Drawing on theories of rhetoric and technology, this article offers an integrated theoretical framework that allows developers of online communities to examine the established rhetorical practices of participants and the ways in which the code of the discursive space may question or facilitate these practices. The paper then illustrates how this framework may be applied to facilitating academic knowledge communication in a wiki space and concludes with implications for decision-making in shaping digital discursive spaces for knowledge communication.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".