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Record W2016076812 · doi:10.5539/hes.v5n1p20

Towards Entrepreneurial Learning Competencies: The Perspective of Built Environment Students

2015· article· en· W2016076812 on OpenAlexvenueno aff
Ernest Kissi, Somiah K. Matthew, Ansah K. Samuel

Bibliographic record

VenueHigher Education Studies · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipPsychologyPerspective (graphical)Graduate studentsEntrepreneurship educationMedical educationKnowledge managementPedagogyBusinessComputer science

Abstract

fetched live from OpenAlex

This paper sought to discuss entrepreneurial learning competencies by determining the outcome of entrepreneurial learning on the views of built environment students in the university setting. In this study, three relevant competencies were identified for entrepreneurial learning through literature, namely: entrepreneurial attitude, entrepreneurial skills and knowledge of entrepreneurship. On this basis, questionnaire was designed and administered to graduate students in built environment. In all, a total of 124 questionnaires were administered to respondents. Out this, 84 were retrieved representing a response rate of 68% and were further subjected to analysis using Relative Importance Index (RII). The findings from the study highlighted on competencies factors that have great impact on entrepreneurs in dealing with tasks and problems related to entrepreneurial learning processes. These key entrepreneurial competencies as perceived by the built environment students were ranked as: entrepreneurial attitude, knowledge of entrepreneurship and entrepreneurial skill. The findings may help stakeholders in the building industry including up-coming graduate students. Thus, it could help in their journey into entrepreneurial terrain affiliated to advancement of their career, as a way to increase private wealth and the pursuit of a more balanced life.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.069
GPT teacher head0.331
Teacher spread0.262 · 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

Citations47
Published2015
Admission routes1
Has abstractyes

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