Modeling Web Accessibility: A Case Study on Texas A&M University People Website
Bibliographic record
Abstract
Statistic analyses are applied to empirical data to test the Internet accessibility. Results indicate a strong statistical correlation between Internet access and computer domain density at international scale, but visit pages/host does not show a correlation with either computer domain density or physical distance. At international scale, access hosts are the most from North America, followed by Europe, Asia, South America, and Africa in a decreasing order. Visit pages/hosts are the highest from China, Japan, and U.S.A, followed by South Korea, Australia, and Canada in a decreasing order among the top 28 countries. At national and regional (Texas) scales, physical distance plays an important role to shape Internet access and visit pages/host. The closer the distance to the Internet server, more access from that physical location occurs. Access hosts have an exponent relationship with distance as gravity model, visit pages/host have a linear and exponent relationship with distance respectively. In the national scope, access hosts are relatively higher from Boston, New Haven (CT), New York, San Jose, and L.A. in comparison with similar distant cities. These may be due to the higher computer domain density in these areas. Statistical analyses suggest that claim of "death in distance" in the information age is misled and digital divide varies in a different scale. The results support "declining importance of distance in individual accessibility"
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".