Sociodemographic Characteristics of Learners and Participation in Computer Conferencing
Bibliographic record
Abstract
This article explores the relationship between learners' sociodemographic characteristics and their level of participation in computer conferencing. A quantitative study of participation among 30 learners in a noncredit agricultural leadership development program provides the empirical data for this exploration. The relationships between learner participation and six sociodemographic variables are explored: sex, age, education level, occupation, residence in urban or rural areas, and region of residence in Canada. Holding a university degree and living in an urban area are found to be the strongest predictors of participation. Recognizing that a considerable amount of variability in learners' participation in computer conferences may reflect those learners' sociodemographic characteristics has important implications for the design and facilitation of such conferences. Cet article explore les relations entre les caracteristiques socio-demographiques des apprenants et leurs niveaux de participation aux conferences telematiques. Une etude quantitative de la participation de trente personnes a un programme non-credite de developpement du leadership en agriculture constitue les donnees empiriques de cette recherche. Les relations entre les apprenants, leurs participations et six variables socio-demographiques sont analysees : le sexe, l'âge, le niveau d'education, l'occupation, le milieu urbain ou rural et le lieu de residence au Canada. Les variables « possedant un diplome universitaire » et « residant en milieu urbain » sont les predicteurs les plus susceptible d'une participation positive des apprenants. Reconnaitre que la grande variabilite de la participation des apprenants aux conferences telematiques peut decouler de leurs caracteristiques socio-demographiques a des implications importantes dans la planification de telles conferences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".