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Record W3091172768 · doi:10.1108/ijshe-11-2019-0323

Implementing social projects with undergraduate students: an analysis of essential characteristics

2020· article· en· W3091172768 on OpenAlexaff
Izabela Simon Rampasso, Renê Grottoli Siqueira, Vitor William Batista Martins, Rosley Anholon, Osvaldo Luíz Gonçalves Quelhas, Walter Leal Filho, Amanda Lange Sálvia, Luis Antonio de Santa-Eulália

Bibliographic record

VenueInternational Journal of Sustainability in Higher Education · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsOriginalityNoveltyScope (computer science)Higher educationEntrepreneurshipSustainabilityValue (mathematics)SociologyPublic relationsPsychologyMedical educationPolitical scienceComputer scienceQualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose This study aims to analyse the essential characteristics for the success of social projects developed with undergraduate students of higher education institutions (HEIs). Design/methodology/approach A case study was conducted to verify the main characteristics of projects in a social entrepreneurship initiative. These features were used to perform a survey with experts to understand which of these items are essential for social projects success, through Lawshe’s method. Findings Of the ten items evaluated, two were considered essential by the experts: “Proper alignment between project scope and actual local community needs” and “Good level of interaction between students participating in the project and the local community”. Practical implications These findings can be useful for professors and coordinators to prepare future projects in HEIs. They may also be advantageous for researchers who may use them as a starting point for future studies. Originality/value The novelty of this study is the methodological approach used: a case study of projects in a social entrepreneurship initiative in a relevant Brazilian university; and a Lawshe’s method analysis of responses of experts in social projects developed in HEIs. The findings can greatly contribute to the debates in this field. No similar research was found in the literature.

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 score0.021
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.336
Teacher spread0.311 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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