Learning in Cyberspace: An Educational View of Virtual Community
Bibliographic record
Abstract
Learning is the creation of knowledge through the transformation of experience and transcends the particular institutional context that society has reserved for that purpose (Cayley, 1992; Illich, 1970; Kolb, 1984). It is also important not to confuse learning exclusively with school knowledge, for knowledge comes in many forms and for different purposes (Barnes, 1988; Dewey, 1938). Using Kolb's view on learning, if we substitute a particular type of change for transformation then change becomes a condition for learning. People participate in learning settings from birth onward. They move from setting to setting such as the home, playground, school, service groups, and church, and over the years add work settings and other leisure activities. Our interests center around creating and conducting inquiry on such learning environments. This particular focus includes both formal school settings, nonschool settings (museums, science centers, public spaces, and the Internet), and the points of intersection between these environments. These interests combine work in both real and virtual, online and off-line spaces. Understanding the nexus of learning and community relies upon an analysis of each context, so as to ascertain the expectations of participants and the task demands of the environment. We accordingly recognize the diversity of virtual environments, and also the interconnections that exist between online and off-line communities. What connects communities, virtual or otherwise, are the possibilities offered for learning; it is not just “school-based” or specifically an educational institution's private preserve.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".