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Record W4386222202 · doi:10.1080/02188791.2023.2251709

Inclusivity of the Hong Kong higher education system: a critical policy analysis

2023· article· en· W4386222202 on OpenAlexaff
Keenan Daniel Manning, Yuet Mui Celeste 袁月梅 Yuen

Bibliographic record

VenueAsia Pacific Journal of Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsConcordia University
Fundersnot available
KeywordsInclusion (mineral)MulticulturalismHigher educationDiversity (politics)Competence (human resources)Public relationsSociologyPolitical scienceHigher education policyPublic administrationPedagogyEducation policyManagementSocial science

Abstract

fetched live from OpenAlex

As centres of higher learning, universities have a unique imperative to promote diversity and inclusion on their campuses. While a supportive campus environment promotes students” intercultural competence and a sense of belonging in the university’s community, a lack of intentional policy structure impedes campus inclusion. This paper aims to examine the institutional policy environment of universities in Hong Kong in order to determine how if at all, institutions in the territory regard inclusion and student diversity on-campus as an institutional value or goal. Using multiculturalism as a lens, we conducted a critical policy analysis of publicly available policy documents from Hong Kong’s 11 universities. We highlighted some of the ways in which universities could expand the inclusion of student voices in the policy-making process. We also highlighted how some general trends, such as the provision of health and wellness services, and specific initiatives, such as targeted financial support for marginalized groups, demonstrate the institutions” commitment to implement effective inclusion policies when needed. However, we have also noted how there are persistent gaps within the coverage of existing diversity policies which continue to create barriers for students to access quality higher education in Hong Kong.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.372
Teacher spread0.350 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2023
Admission routes1
Has abstractyes

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