АНАЛІЗ ДОСТУПНОСТІ ВЕБРЕСУРСІВ ДЛЯ УЧАСНИКІВ БОЙОВИХ ДІЙ
Bibliographic record
Abstract
Information technology support for veterans consists of providing comprehensive support for their reintegration using information technologies, of which the most common are information web resources. An urgent task is to improve the information and technological support of the military and people whose traumatic experience has affected their physical and mental health. Information technologies, before recommending them for use in military reintegration processes, should be evaluated for compliance with global standards of web content accessibility. In this article, we pay attention to the work of services that automatically evaluate web resources for compliance with such requirements. Compliance with the requirements of the Guidelines on the accessibility of web content is relevant for the Ukrainian online space not only for compliance with European standards but to a greater extent - due to the need to satisfy the information needs of veterans who may require a special presentation of information due to the received injuries. The purpose of this study is to analyze the work of the specified online services for assessing the availability of web resources relevant to combatants, using the examples of official pages of state institutions that provide information support to the military, combatants, and veterans of various countries - Israel, the USA, Canada, and Ukraine. The formation of the IT support system will make it possible to improve the process of reintegration of combatants. Evaluating the compliance of web resources with the requirements of the international standard will make it possible to recommend access to web content to military personnel who may have injuries of various etymologies, considering the physical and cognitive features of the post-traumatic state. The research compared the results of web accessibility assessment of the websites of government organizations of different countries (Israel, Canada, USA, and Ukraine) using available resources – AChecker, WAVE, and Access Monitor. The combined use of such resources makes it possible to assess the compliance of online resources more fully, such a study should also be expanded to other resources that are relevant for the reintegration of the military and combatants of Ukraine.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".