The Combined Effects of Response Time and Message Content on Growth Patterns of Discussion Threads in Computer-Supported Collaborative Argumentation.
Bibliographic record
Abstract
This study examined the effects of response time and message content on the growth patterns of discussion threads in computer-supported collaborative argumentation. Event sequence analysis was used to measure response times between threaded messages and responses containing arguments, evidence, critiques, evaluations, and other comments from online debates. The results supported and contradicted the findings of Hewitt and Teplovs (1999). Response rates overall declined at a rate of 17% per day in wait time across all message categories. On the other hand, the posting of critiques and particular types of argumentative exchanges produced higher response rates of .72 and higher, and their average wait times of 1.04 days were significantly longer than those of other message types. The debate format and use of message labels may have produced sufficient argumentative exchanges to produce high response rates despite the long response times, which in turn helped sustain the growth of discussion threads. L’étude examine les effets du temps de réponse et du contenu du message sur les modèles de croissance du volume des discussions dans l’argumentation collaborative assistée par ordinateur. L’analyse séquentielle des événements a été utilisée pour mesurer les temps de réponse, i.e. le temps entre les messages envoyés et les réponses reçues qui contenaient des arguments, des preuves, des critiques, des évaluations et d’autres commentaires issus des débats en ligne. Les résultats ont appuyé et contredit les résultats de Hewitt et Teplovs (1999). D’une part, le rythme des réponses a diminué dans l’ensemble, à un taux de 17 % par jour en temps d’attente dans toutes les catégories de message. D’autre part, les articles de critique et d’argumentation ont produit des taux de réponse plus élevés, soit de 72 % et plus, et leur temps d’attente moyen, 1,04 jours, était nettement plus long que celui des autres types de message. Il est possible que la formule débat et l’utilisation de labels pour identifier les messages aient produit suffisamment d’échanges d’argumentation entraînant des taux de réponse élevés, malgré les longs temps de réponse, qui à leur tour ont favorisé la croissance du volume des discussions.
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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 | 0.041 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".