CATTW I ACPRTS Annual Conference Congress of the Social Sciences and Humanities/Congres annuel de la Federation canadienne des sciences humaines et sociales
Bibliographic record
Abstract
Session ThemesI. Challenging boundaries: Inter-disciplinary and inter-media approaches to genre and discourse practices• Disciplines such as Anthropology, Linguistics, and Literary Theory are currently struggling with key issues in the discussion of genre.What value do these approaches have for researchers and teachers in technical and professional communication?• Do genres traverse different disciplines or communities of practice?And what happens to text types when they occur in different contexts?• Is cyberspace a new, qualitatively different realm of discourse practices and genres?Does the www call for new understandings of rhetorical issues such as social context, writer-reader relationships, readability/usability and ethos?• Are the new discourse communities emerging on the web creating new genres, or reshaping existing genres, in ways that reflect novel values, communication priorities and writing/reading habits?Is it still possible for on-line communities to remain local and culturally unique, and to adapt written genres to their own particular needs?In oilier words, is the new medium creating a cultural hegemony or allowing for the play of differences?• If the www creates new contexts for writing, what does this imply for our conceptions of rhetorical expertise and for our teaching practices?Technostyle Vol.17, No. 2 2002
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.013 |
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".