"Online Communities Research: Quo Vadis? Perspectives on Knowledge Work, Collaboration, & Innovation"
Bibliographic record
Abstract
Research on online communities has once and for all entered the field of organization and management studies. We currently witness a growing number of publications on the subject of online communities in top journals in the field. Furthermore, numerous sessions at last year’s annual meeting of the Academy of Management were dedicated to the topic. Online communities are often described as new organizational forms or meta-organizations, in which knowledge collaboration can take place on an unprecedented scale. They may be employed both within firm boundaries and beyond them, enabling organizations to engage in open and user innovation. They also constitute an important ingredient in many firms’ digital business strategy. After some 20 years of research into online communities, it is time for taking stock of existing research and looking ahead into the future of the field. The purpose of this symposium is to provide a forum of exchange for management researchers interested in online communities. Emphasis will be given to how online communities are used for knowledge work, collaboration, and innovation. To that end, we have enrolled top scholars in the field of online communities research who will present their views and experiences. Several prevalent issues will be raised during a facilitated discussion. Participants will also have the opportunity to ask questions and learn from their colleagues when it comes to designing and conducting future online community studies.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".