Towards a systematic approach for the credibility of humancentric web applications
Bibliographic record
Abstract
The apparent socialization of the Web brings new prospects as well as challenges. In this paper, the issue of credibility of Web Applications in the light of increased human participation and collaboration is considered. The stakeholder types to which credibility of Web Applications is relevant are identified. Based on a taxonomy of credibility, the origins of the issue of credibility specific to human-centric Web Applications are explored and examples in support are presented. The role of addressing credibility within the auspices of flexible and iterative development processes is emphasized. A framework for understanding and addressing the credibility of human-centric Web Applications in a methodical manner is proposed. This framework includes quality attributes of concern to stakeholders and process- and product-oriented means for addressing them in a feasible manner. Finally, extensions of the framework, including implications towards the Semantic Web, are briefly outlined.
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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 | 0.132 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.007 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".