The Accessibility Levels of the Websites of Federal Higher Education Institutions in Northeast Brazil
Bibliographic record
Abstract
The general aim of this study is to make a diagnosis on the accessibility of information made available on the websites of the Federal Higher Education Institutions (IFES) in the Northeast. It is based on the hypothesis that the websites of the IFES in the Northeast Region of Brazil do not have high levels of adequacy, results that do not allow any user, regardless of being a person with disabilities or other limitations, to access the knowledge made available there, either with the help of assistive technologies or autonomously. The methodological procedures will be bibliographical and documentary research with analysis of primary sources on institutional websites and administrative acts made available, or not, on these sources. All the open data portals will be evaluated and simulated to determine their suitability for eMAG. This will be done using the public software ASES, a system for comparing and validating the standards of construction and behaviour of the source code of electronic sites, pointing out the levels of usability, navigability, alternative text, and content markers regarding the eMAG parameters, which are mandatory for public institutional sites in Brazil in terms of digital accessibility. As a result of the research, it was possible to identify that the levels of accessibility on the websites of these institutions were not in line with the parameters of the Digital Government Accessibility Model (eMag) of Brazil. This reveals divergences in the promotion of access to information; while some IFES make information available autonomously, without facing noise, interference, or impediments to the use of information, others do not so much, which has a negative impact on the guarantee of rights such as the exercise of full citizenship.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".