New directions in online community research
Bibliographic record
Abstract
Information Systems researchers have studied multiple forms of online communities for decades. Significant progress has been made in addressing research questions such as how and when individuals are motivated to contribute knowledge in online settings. Yet, not only are important questions unanswered—such as why online communities succeed or fail—but also there still remains disagreement on the basic definition of online community. Furthermore, as the diversity of users and uses of online media continues to increase, IS researchers can now ask and answer different questions. For example, advances in social computing, mobile computing, and social media support new forms of online communities. In this panel we will propose and debate the direction of an online community research agenda for the next decade and beyond.
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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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.014 | 0.088 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".