The Combined Effects of Response Time and Message Content on Growth Patterns of Discussion Threads in Computer-Supported Collaborative Argumentation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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’etude examine les effets du temps de reponse et du contenu du message sur les modeles de croissance du volume des discussions dans l’argumentation collaborative assistee par ordinateur. L’analyse sequentielle des evenements a ete utilisee pour mesurer les temps de reponse, i.e. le temps entre les messages envoyes et les reponses recues qui contenaient des arguments, des preuves, des critiques, des evaluations et d’autres commentaires issus des debats en ligne. Les resultats ont appuye et contredit les resultats de Hewitt et Teplovs (1999). D’une part, le rythme des reponses a diminue dans l’ensemble, a un taux de 17 % par jour en temps d’attente dans toutes les categories de message. D’autre part, les articles de critique et d’argumentation ont produit des taux de reponse plus eleves, soit de 72 % et plus, et leur temps d’attente moyen, 1,04 jours, etait nettement plus long que celui des autres types de message. Il est possible que la formule debat et l’utilisation de labels pour identifier les messages aient produit suffisamment d’echanges d’argumentation entrainant des taux de reponse eleves, malgre les longs temps de reponse, qui a leur tour ont favorise la croissance du volume des discussions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it