Understanding and analysing activity and learning in virtual communities
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
Abstract The purpose of this study is to provide a preliminary framework to observe, analyse and evaluate both activity and learning in virtual communities. So various types of virtual communities will be studied by examining their relationship to socialisation and learning. After a presentation of the main ideas of Wenger's social learning theory, the principal components of the social context of the emergence and evolution of virtual communities will be described. It will show how taking this context into account enables the definition of four principal types of virtual communities: community of interest, goal‐oriented community of interest, learners' community and community of practice and describe how the activity of these communities develops according to the goals they set for themselves and to the strategies they adopt to reach them. For each type of virtual community, an attempt will be made to determine the process of negotiation of meaning at the base of learning, and to describe the learning performed in terms of participation and reification processes.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| 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