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Record W3174687271 · doi:10.1108/et-10-2020-0310

Entrepreneurship education and training in Indian higher education institutions: a suggested framework

2021· article· en· W3174687271 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducation + Training · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOriginalityMainstreamExperiential learningEntrepreneurshipHigher educationNarrativeMeaning (existential)Value (mathematics)PedagogyQualitative researchSociologyPsychologyPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Purpose The study aims to evaluate the components of entrepreneurship education and training (EET) in India. The paper proposes a framework for an effective EET regime for amalgamating entrepreneurship education as fundamental to mainstream higher education in India. Design/methodology/approach The current study utilises a qualitative research technique, that is, the narrative inquiry methodology based on in-depth interviews. The study respondents included sixteen educators who are actively engaged in EET and related activities for a minimum of ten years. Findings The study identified five broad “meaning units” or “themes,” that is, “incremental pedagogical efficiency and flexible evaluation systems,” “entrepreneurial experience of the faculty,” “extended support,” “holistic mentoring” and “experiential learning” as components of an effective EET regime. Originality/value The study will help the policymakers and higher education institutions (HEIs) revisit their policy frameworks and practices to promote entrepreneurial capacity and entrepreneurial intentions among students. The study will also help to gain deeper insights into EET components and will propose a framework for an effective EET regime based on its findings.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.061
GPT teacher head0.309
Teacher spread0.247 · 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