Online access, participation and information credibility assessment
Bibliographic record
Abstract
The Web today is as much a social space with a participatory culture spurred by online collaboration technologies, as it is an information source. The possibility that almost anyone can contribute content has made credibility assessment online important and difficult at the same time. Empirical studies claim that online credibility perceptions are based more on heuristics than on systematic processing of information and that they are greatly influenced by visual design and visual cues. These studies did not include participants using non-visual modalities to interact with the Web. To begin to address this, we performed a study of 60 Web users with visual impairments, who interact non-visually with the Web using a screen reader and keyboard. Among the findings of our study were: 1) The ability of visually impaired users to assess online information credibility is governed by the level of Web accessibility in context; 2) Their credibility assessments are supported by social factors, marked by collaboration with their information community; and 3) Visually impaired users are generally unable to use recent online collaboration technologies to the same extent as their sighted peers reportedly do, because these technologies are not very accessible to them. Our study also confirmed that for effective use of online resources, full access is important not only to online information but also to the online participatory culture, because information credibility assessment is a social process that requires a trusted community and social tools to support it. Using a participatory design process, we built an accessible and interactive online community website with social tools to provide inclusive online support as a proof-of-concept that websites can support accessibility and interactivity at the same time and these are not mutually exclusive as commonly believed.
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 | 0.018 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".