Developing Research Skills in Criminology Students Through Interdisciplinary Approaches
Bibliographic record
Abstract
This study examines the effect of interdisciplinary approaches on the development of research skills among criminology students at St. Francis Xavier College. Recognizing the critical need for research competence in criminology, this study examines how integrating insights from various disciplines, such as law, psychology, and sociology, enhances students' abilities to formulate research questions, design methodologies, and analyze data. Utilizing a quantitative, descriptive-correlational research design, data were collected from 245 criminology students at St. Francis Xavier College through a structured survey questionnaire. Findings indicate that interdisciplinary approaches, particularly curriculum integration, effective teaching strategies, and student engagement, significantly influence the development of research skills. Results show very high levels of both interdisciplinary approaches and research skills, with strong positive correlations between the two variables. Notably, student engagement was found to have the most substantial impact on research attitude and confidence. The study underscores the importance of adopting interdisciplinary frameworks in criminology education to foster critical thinking and evidence-based practice. Recommendations include enhancing curriculum design to reflect interdisciplinary connections, employing interactive teaching strategies, and promoting student engagement through collaborative research activities. This research contributes to the understanding of how interdisciplinary learning can prepare criminology students for effective research and professional practice, aligning with the goals of quality education and sustainable development.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".