Discourse 2.0: Language and New Media
Bibliographic record
Abstract
Introduction Deborah Tannen and Anna Marie Trester, Georgetown University 1. Discourse in Web 2.0: Familiar, Reconfigured, and Emergent Susan C. Herring, Indiana University-Bloomington 2. Polities and Politics of Ongoing Assessments: Evidence from Video-Gaming and Blogging Herve Varenne, Gillian Gus Andrews, Aaron Chia-Yuan Hung, and Sarah Wessler, Teachers College, Columbia University 3. Participatory Culture and Metalinguistic Discourse: Performing and Negotiating German Dialects on YouTube Jannis Androutsopoulos, University of Hamburg 4. My English Is So Poor...So I Take Photos: Metalinguistic Discourses about English on Flickr Carmen Lee, Chinese University of Hong Kong 5. Their Lives Are So Much Better Than Ours!: The Ritual (Re)construction of Social Identity in Holiday Cards Jenna Mahay, Concordia University Chicago 6. The Medium Is the Metamessage: Conversational Style in New Media Interaction Deborah Tannen, Georgetown University 7. Bringing Mobiles into the Conversation: Applying a Conversation Analytic Approach to the Study of Mobiles in Co-present Interaction Stephen M. DiDomenico, Rutgers University and Jeffrey Boase, Ryerson University 8. Facework on Facebook: Conversations on Social Media Laura West and Anna Marie Trester, Georgetown University 9. Mock Performatives in Online Discussion Boards: Towards a Discourse-Pragmatic Model of Computer-Mediated CommunicationTuija Virtanen, Abo Akademi University 10. Re- and Pre-authoring Experiences in Email Supervision: Creating and Revising Professional Meanings in an Asynchronous Medium Cynthia Gordon and Melissa Luke, Syracuse University 11. Blogs: A Medium for Intellectual Engagement with Course Readings and ParticipantsMarianna Ryshina-Pankova and Jens Kugele, Georgetown University 12. Reading in Print or Onscreen: Better, Worse, or About the Same? Naomi S. Baron, American University 13. Fakebook: Synthetic Media, Pseudo-sociality, and the Rhetorics of Web 2.0 Crispin Thurlow, University of Washington Index
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".