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Record W4394540103 · doi:10.6084/m9.figshare.5816565

Accessibility, Gender and Higher Education: Indicators from Scientific Research

2018· dataset· en· W4394540103 on OpenAlexaboutno aff
Jackeline Susann Souza Da Silva, Francisca González-Gil

Bibliographic record

VenueFigshare · 2018
Typedataset
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsData scienceComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.7950.009

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.

Opus teacher head0.326
GPT teacher head0.502
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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