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Record W4396861066 · doi:10.5430/jct.v13n2p159

Academic Staff Commitment towards Implementing Curriculum from Multicultural Perspectives in Eastern Ethiopia Higher Education

2024· article· en· W4396861066 on OpenAlexvenueno aff
Debela Tezera Simagn, Yilfashewa Seyoum, Dawit Negassa, Garkebo Basha

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMulticulturalismPedagogyMulticultural educationSociologyMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This study aimed to investigate academic staff commitment to implementing curriculum from multicultural perspectives in Eastern Ethiopian higher education. This problem is instigated by the long-term ignored area or multicultural issues in teaching and learning process in higher education in Ethiopia which, in turn, has become a basis of the increase of plentiful difficulties. To achieve the intended objective, a descriptive design was employed. The data were collected through a self-administered questionnaire and analyzed using descriptive and inferential statistics. The study revealed that the commitment of academic staff was low and there was no gender difference in implementing curriculum. Based on the findings, it is concluded that if academic staff had been committed and used culturally responsive pedagogy, the implementation of a multicultural curriculum would have been ensured. Therefore, based on these findings implications were made for future research and suggested addressing and alleviating salient problems.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.027
GPT teacher head0.385
Teacher spread0.359 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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