The Occurrence and Character of Stories and Storytelling in a Computer Conference
Bibliographic record
Abstract
Constructivist views of online interaction often refer to the power of stories and the role of storytelling in the sharing and construction of knowledge, and the creation of learning communities. No empirical evidence of the presence or character of stories in online conferences has been systematically reported, however. This study describes the occurrence of stories in a computer‐mediated communication (CMC) transcript generated by experienced online communicators (graduate students), in relation to some of the expectations of a constructivist view of narrative in online interaction, and in contrast with a historical model for describing face‐to‐face interaction (Bales, 1950 Bales, R. F. 1950. A set of categories for the analysis of small group interaction. American Sociological Review, 15(2): 257–263. [Crossref], [Web of Science ®] , [Google Scholar]). Findings include the observation that, while stories occurred in about one posting in five, students used stories markedly more often than the instructor‐moderator; stories tended to be descriptive, rather than analytic, advisory, or hortatory; gender was not an issue in story use; and both story and non‐story postings were highly group‐supportive, providing information and answers to questions, and avoiding negative social interactions (a finding noted previously in moderated, academic conferences).
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".