Inicijative za promicanje pristupačnosti elektroničkih informacija osobama s invaliditetom
Bibliographic record
Abstract
This paper presents an overview of the developments that led to the establishment of standards for management and\ndefinition of technological, economic and social aspects of accessibility of Internet sites and electronic documents for persons with disabilities. The persons with disabilities are being discriminated due to a lack of accessibility of electronic information.\nAccessibility of electronic resources is not limited to disability related issues only but it also takes into account situational, social and cultural aspects . Indeed, those aspects do not exclude functionality as an important criterion. They include cultural, social,gender and situational aspects as the use of Internet should not be based solely on a person’s disability. Other aspects of personality should not be neglected either. The development of free software has enabled easier implementation and the development of web applications developed according to the accessibility guidelines. Free software ATutor is presented in this paper as a successful and viable application for accessible learning content management. Free licensing enables everybody to use this software freely.\nLegal regulations enacted in EU, USA, Canada, Australia and other states give an example of how this issue can be legally regulated in a successful way. A variety of civic, governmental, inter-governmental associations and institutions strive 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".