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Record W4210748546 · doi:10.5430/ijhe.v11n4p49

Building an Excellence Capabilities Portfolio in Higher Education: A Case Study of Higher Colleges of Technology, UAE

2022· article· en· W4210748546 on OpenAlexvenueno aff
Addel Al Ameri, Ahmed Ghonim

Bibliographic record

VenueInternational Journal of Higher Education · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceDynamic capabilitiesOperational excellencePortfolioStakeholderKnowledge managementMaturity (psychological)Process managementEngineering managementCapability Maturity ModelHigher educationBusinessResource (disambiguation)Computer scienceEngineeringSoftwarePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

The Higher Colleges of Technology (HCT) has developed and deployed a set of distinctive and dynamic capabilities in alignment with the UAE national agenda. These capabilities drive an ambitious strategy that anticipates and ensures future readiness; creates student-centric value; enables and empowers human capital; exploits the full potential of digital enablement; leverages through smart partnerships and resource optimization; builds capacity for knowledge exchange; and synergizes through collaboration, co-creation, and stakeholder orientation. This study presents HCT’s methodology as a case study to develop its capabilities portfolio, structure, main connectors (internal and external forces), and business operational model and capabilities dynamics that support HCT’s continuous transformation and leading position. The study proposes a model and practices to develop and unify standards for higher education institutions (HEIs) in their journey towards building distinctive and dynamic capabilities and achieving excellence maturity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.198
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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