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Record W3173807302 · doi:10.24908/pceea.vi0.14969

EFFECT OF MENTORSHIP ON THE EARLY ENTREPRENEURIAL JOURNEY OF UNIVERSITY STUDENTS

2021· article· en· W3173807302 on OpenAlexafffundvenue
Loujein Mouammer, Carlos Bazán

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsMentorshipGraduation (instrument)EntrepreneurshipCredibilityThematic analysisMedical educationValue (mathematics)PsychologyPerceptionPedagogyManagementSociologyQualitative researchMedicinePolitical scienceEngineeringSocial scienceComputer science

Abstract

fetched live from OpenAlex

Acknowledging the value of entrepreneurs in today’s society, universities are looking into mentorship to improve their support system for students considering entrepreneurship as a viable career path after graduation. This paper reports the main findings of a systematic literaturereview aimed at understanding the role that mentorship plays during the early entrepreneurial journey of university students. That is, identifying what motivates students to seek mentorship and recognizing the critical elements of successful mentorship programs that develop robustmentor-mentee relationships. The systematic literature review classified the selected articles into three thematic categories: mentoring in entrepreneurship, gender differences in mentoring, and mentorship programs in business incubation. Findings in the combined categories suggest that a positive mentoring experience depends on the mentor’s credibility and connection inthe business world and the mentee’s perception of similarities between their mentor and themselves.

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)0.017
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.106
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.250
Teacher spread0.240 · 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 designQualitative
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

Citations9
Published2021
Admission routes3
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicMentoring and Academic DevelopmentFrench-language works237,207