Bibliographic record
Abstract
Trust is one of the key factors that emerged as a significant concept in virtual communities. Trust is so complicated that it is hard to define in one standardized way. Trust issues have evolved into two major ways in the fields of virtual community and security. Among a huge literature concerning trust in virtual communities, a majority of literature addresses technical solutions on trust-building by providing new Web-based applications. They range from human users authorization, semantic Web, agent technologies and access control of network to W3C standardization for content trust and security. Some examples include AT&T’s Policymaker or IBM’s Trust Establishment Module (Blaze, Feigenbaum, & Lacy, 1996; Herzberg, 2000). Only a minority deals with understanding the concept of trust and sources of trust-building from social and cultural aspects. It appears to miss the essence of trust in virtual communities, although an integrated approach is needed for building trust in communication and the use of virtual communities. This article aims to present the definition of trust and relevant concepts for recognizing sources of trust-building in virtual communities. This article also presents future research implications for further development on trust and trust-building in virtual communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".