Energizing Engagement and Motivation in Information-Centric Online Communities: LibraryThing, Goodreads, and the Importance of Boundary Spanning
Bibliographic record
Abstract
This paper focuses on key implications for engagement and motivation in online communities from a study of LibraryThing and Goodreads and the roles they play as boundary objects in existing and emerging communities. The design, development, leadership, and administration of information-centric online communities should highlight and facilitate the creation and sharing of translation processes and resources; make clear expressions of and continually negotiate community norms, values, and normative behaviours; and support and facilitate—but not force—social tie formation and everyday life information behaviour. Cet article s’intéresse aux enjeux clés pour l'engagement et la motivation au sein des communautés en ligne s’appuyant surune étude de LibraryThing et de Goodreads et des rôles que ces plateformes jouent dans les communautés existantes et émergentes. La conception, le développement, le leadership et l'administration des communautés en ligne centrées sur l’information devraient mettre en évidence et faciliter la création et le partage des processus et des ressources; produire des expressions claires et continuellement négocier des normes et valeurs communautaires; ainsi que de soutenir et faciliter – mais non pas forcer –la formation de liens sociaux et de comportements informationnels dans la vie de tous les jours.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".