Accessibility, Gender and Higher Education: Indicators from Scientific Research
Bibliographic record
Abstract
ABSTRACT This article aims to present a literature review of texts that articulate accessibility, gender and higher education published from 2013 to the current period. To place the object of study within the wide dissemination of scientific works, researchers must choose a method that includes information relevant to the research problem. This phase of the investigation, therefore, involves the choice of criteria for search management and procedures for comparing, categorizing and analyzing publications among themselves and with the research to be developed. In this context, the purpose of this text is to disseminate an alternative to literature review design made from the categorization and analysis carried out through NVivo 11 Program and the detailed reading of 72 papers developed in Brazil, Spain, Colombia, Chile, Mexico and Canada. With the search software, the identification of the most frequent words allowed the elaboration of a conceptual mosaic that guided a discussion of the present research. However, the words lacking in the search procedure, but important for the development of the thesis - such as gender, feminism and woman - reveal the need to introduce these terms into an investigative work. The result of this literature review shows that the study on accessibility in higher education is developed predominantly by women and that the multidimensionality of the meaning of accessibility causes researchers to seek answers in other fields of knowledge, thus escaping the limits of the hyper-specialization of Special Education. This aspect can contribute to innovation and advancement of knowledge in focus.
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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 | 0.012 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.092 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".