Minitrack: "Online Communities in the Digital Economy"
Bibliographic record
Abstract
Some years ago, Online Communities were considered one of the most promising innovations resulting from the Internet revolution. Community building and community development were proclaimed to be a key success factor for the digital enterprise. As a result, Internet ventures tried to artificially build and foster Online Communities in different forms – as part of online shops, portal sites or B2B platforms, or as design, relationship or gaming communities. At the same time research was mainly related to topics as for example how to build a community and how to gain critical mass and market shares as soon as possible. Today, findings show that in many cases Online Communities did not meet the expectations of their operators. Only a few Online Communities are financially sustainable, many disappeared and in many cases companies could not get the promised gains out of their online ventures. Consequently, the most important research questions concerning Online Communities are related to the investigation of factors for success or failure (financially as well as socially) by means of longitudinal studies. A related and lately emerging research area considers new forms of Online Communities – the so called Mobile Communities. This minitrack comprises a series of papers that study success and failure of Online Communities and their respective business models. The papers provide longitudinal studies, discussion of social aspects, case studies, and address critical aspects of community building.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".