Bibliographic record
Abstract
Computer mediated conferencing (CMC) has been widely viewed as a valuable forum for providing opportunities for interaction among learners in a distance education setting. Interaction in distance contexts; however, is not well understood, and it has been argued that social markers are cued in online communications and that gender influences interaction processes and participation. Earlier research has identified two discourse types, epistolary and expository, that have been associated with gender. This study examined the discourse patterns and styles of 37 women and 27 men involved in a graduate course that used computer conferencing. Predicted patterns of discourse were found; women tended to use more epistolary or aligned type statements, and men tended to use more expository type statements. An investigation of satisfaction, commitment, and purpose for interaction indicated that level of satisfaction with interactions was similar, but evidence of gender-related purposive differences and a higher participation rate for men were found. Les conférences télématiques sont largement considérées comme un bon moyen d’offrir des opportunités d’interactions entre apprenants en situation d’éducation à distance. L’interaction dans le contexte de la distance, cependant, n’est pas bien comprise, et il a été avancé que les marqueurs sociaux sont déterminés dans les communications en ligne et que le genre influence les processus d’interaction et de participation. La littérature identifie deux types de discours, épistolaire et expositoire, qui sont associés au genre. Des formes prévisibles de discours ont étés trouvées. Les femmes ont tendance à utiliser des énoncés épistolaires, alors que les hommes utilisent plus d’énoncés expositoires. Une étude de la satisfaction, de l’engagement et du but de l’interaction indique que le niveau de satisfaction avec les interactions était similaire, mais il y avait indication de différences dans le but reliées au genre et un taux de participation plus élévés pour lese hommes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".