Implementing social projects with undergraduate students: an analysis of essential characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".