Socialization Through (Online) Design: Moving into Online Critical Spaces of Learning
Bibliographic record
Abstract
This research investigates the social and socialization practices within an online professional development web seminar. The aim of this study was to identify the kinds of social and community building practices that occur in online professional development webinars by exploring how communication tools such as chat are used for community building and socializing purposes. Data was comprised of all electronically generated web seminar (webinar), written chat comment transcripts that were automatically generated during this series of webinars. Data were analyzed inductively and recursively using the constant comparative method. Findings revealed that the Online Web Seminars in Literacy project (oWSL) built community through moderators and participants greeting, assisting, and offering one another support. Moreover, social practices found within and across seminars included social talk, thoughtful debate, and the formation of nested affinity groups. This research revealed that this online professional development webinar provided a space where social practices like creating a sense of community through mutual support and engaging in productive disagreement among participants can stimulate informative critical dialogue that webinar organizers can draw upon to form dynamic and productive online professional development communities.Keywords: Online communities of practice; socialization; web seminars (webinars); professional development; critical literacy
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".