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Record W4400066153 · doi:10.47989/kpdc518

Equality in higher education opportunities: Practitioners’ perspectives from global, rural, post-colonial disability

2024· article· en· W4400066153 on OpenAlexaff
John C. Hayvon, Victor John Cordeiro, Jane Dunhamn, Susanne Strömberg Jämsvi, Jess Stainbrook, Nidhi Singhal

Bibliographic record

VenueJournal of Praxis in Higher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsColonialismSociologyPolitical scienceGender studies

Abstract

fetched live from OpenAlex

This paper gathers practitioner perspectives on tuition-free online courses and their potential to improve equality in higher education. Through an intersectional lens of race, gender, income, and indigeneity, this paper focuses on the experience of people living with disabilities (PLWD) as a further marginalized sub-population within diverse marginalized populations. Of note, disability-knowledge held by PLWD and by their family members can position them as sensitive and effective healthcare or disability-care providers. At the same time, society often does not grant an easy pathway to this education and licensure. The existing landscape of massive open online courses (MOOCs) may present tuition-free learning, but accreditation can rest upon payment and other complex structures. Even after PLWDs gather financial resources for official accreditation, prospective employers have the autonomy to determine whether this learning is valid. In a global context, low-income families may experience internal competition for financing between PLWD and non-disabled siblings. Securing a future in which payment models and disability-needs are accommodated for in MOOCs can alter multiple life trajectories in the families of PLWD and ensure that the intersectionally marginalized may equally benefit from open education.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.119
GPT teacher head0.444
Teacher spread0.325 · 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 teacher head, not a consensus.

Study designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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