Developing a Web Credibility Evaluation Tool Using PROMETHEE Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, the Internet has become an indispensable way for users to find information which is almost instantaneously available. However, the presence of information on different websites makes the user needs to pre-check the credibility of the selected websites. Most users find it difficult to assess website credibility in terms of its particular characteristics or factors. Accordingly, we proposed an automated evaluation tool which considers various factors to assess the credibility of different websites and rank them from the highest credibility score to the lowest in order to allow the user to select the most credible website. We used the Preference Ranking Organization Method for Enrichment Evaluations (PROMOTHEE). The latter is one of the Multi-Criteria Decision Making methods (MCDM). It combines pairwise comparison and outranking methods in order to give more accurate and superior credibility scores due to its enrichment evaluations. For the proposed tool to be acceptable, we carried out a correlation analysis to determine the coefficient of correlation between human judges and the proposed tool. We found the coefficient of correlation rho is 0.943 which indicates that there is a strong correlation between the human judges’ ranking and the ranking given by the proposed website evaluation tool.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.014 |
| Open science | 0.000 | 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 it