Bibliographic record
Abstract
This paper presents an overview of the developments that led to the establishment of an idea of accessibility of electronic information for people with disabilities. The development of legal and technological standards influenced economic, social, legal and technological aspects of accessibility of the Internet sites. A lack of accessibility and usability of electronic resources for people with disability is a practice of discrimination of people with disabilities. However, accessibility itself is not solely based on aspects of functionality. Accessibility assumes aspects of situational, cultural and social aspects of accessibility. Those aspects do not exclude functionality as an important criteria. Indeed, they include cultural, social, gender, situational aspects due to the fact that person with disability should not be treated soley on his/her disability. All other aspects of personality should be respected too. The development of feee software enabled easier implementation and the development of web applications developed according to the rules of accessibility. Flexible licensing enabled everyone to use free software web applications freely to everyone. Free software ATutor is presented as successful and viable application for accessible education management. Legal regulations enacted in EU, USA, Canada, Australia and other states give an example how this issue can be legally regulated in a successful way. A variety of civic, governmental, inter-governmental associations and institutions strive and foster on local and international levels to promote and implement standards and legal regulations in the field.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".