Entrepreneurial Intention in High School: Systematic Literature Review of the Period 2000–2022
Bibliographic record
Abstract
Researchers have devote little attention to exploring entrepreneurial intention (EI) in high school education. The lack of academic papers that seek to analyze the state of academic production of EI in high school education opens a gap in the academic literature. This study aims to analyze the academic production of EI in high school in the period 2000–2022, adopting the systematic literature review. The study is descriptive, quantitative and exploratory in nature, using the Scopus, Web of Science and ERIC databases and the Bibliometrix tool. After using the screening protocol 33 articles were selected for analysis. One of the main findings of the study is the mapping of four thematic lines: a) testing conceptual model of EI; b) influence of different factors on the EI of high school students; c) EI can be studied using different constructs; d) validation of the EI questionnaire. The findings are innovative and contribute to filling a gap in the academic literature. The results have many practical implications. For example: a) high school principals, coordinators and teachers can use the results to stimulate reflection on the centrality of the student in the teaching and learning processes, aiming to develop entrepreneurial competencies and skills; b) thematic lines could help in buiding a research agenda with several research avenues.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.032 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".