MétaCan
Menu
Back to cohort
Record W2896886775 · doi:10.5539/hes.v8n4p104

Inclusive Higher Education for Students with Disabilities in China: What Do the University Teachers Think?

2018· article· en· W2896886775 on OpenAlexvenueno aff
Yuexin Zhang, Sandra Rosén, Li Cheng, Jingshan Li

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHigher educationInclusion (mineral)Affect (linguistics)ChinaPedagogyPerspective (graphical)Medical educationPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Inclusive higher education is a path to protect the educational rights of university students with disabilities. University teachers’ attitudes toward students with disabilities, and towards their inclusion in universities, are a key factor that will affect the development of inclusive higher education. This study used a questionnaire to explore an overall perspective of how university teachers in China view inclusive higher education from emotional, cognitional and conative aspects. Their responses suggest that university teachers in China have positive emotion and cognition toward the rights of students with disabilities to receive higher education; the teachers do, however, appear to lack motivation, relevant knowledge, skills, and effective strategies to cope with the students’ special needs. This suggests that effective implementation of inclusive higher education must be supported by an effective service center for those who have disabilities, a support network of professionals, and an administrative support system for teachers and students.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.047
GPT teacher head0.412
Teacher spread0.365 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations57
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHigher Education StudiesSame topicDisability Education and EmploymentFrench-language works237,207