Bibliographic record
Abstract
In recent years, there has been a steady shift in the nature of Web applications. The vehicle of this transition of Web applications is us, the people. The ability to post photographs or videos, exchange music snippets with peers, and annotate a piece of information, are but a few exemplars of this phenomenon. Indeed, the pseudonym Web 2.0 (O’Reilly, 2005) has been used to describe the apparent “socialization” of the Web. In spite of the significant prospects offered by humancentric Web applications, the mere fact that virtually anyone can set up such applications claiming to sell products and services or upload/post unscrutinized information on a topic as being “definitive,” raises the issues of credibility from a consumers’ viewpoint. Therefore, establishing credibility is essential for an organization’s reputation and for building consumers’ trust. The rest of the article is organized as follows. We first provide the background necessary for later discussion. This is followed by the introduction of a framework within which different types of credibility in the context of human-centric Web applications can be systematically addressed and thereby improved. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".