Mapping the Complexities of Online Dialogue: An Analytical Modeling Technique
Bibliographic record
Abstract
The e-Dialogue platform was developed in 2001 to explore the potential of using the Internet for engaging diverse groups of people and multiple perspectives in substantive dialogue on sustainability. The system is online, text-based, and serves as a transdisciplinary space for bringing together researchers, practitioners, policy-makers and community leaders. The Newell-Dale Conversation Modeling Technique (NDCMT) was designed for in-depth analysis of e-Dialogue conversations and uses empirical methodology to minimize observer bias during analysis of a conversation transcript. NDCMT elucidates emergent ideas, identifies connections between ideas and themes, and provides a coherent synthesis and deeper understanding of the underlying patterns of online conversations. Continual application and improvement of NDCMT can lead to powerful methodologies for empirically analyzing digital discourse and better capture of innovations produced through such discourse. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs140221
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".