Students leading students: a qualitative study exploring a student-led model for engagement with the sustainable development goals
Bibliographic record
Abstract
Purpose Higher education institutions (HEI) play a critical role in developing student leaders equipped with the skills and knowledge needed to mobilize societal changes that the United Nations Sustainable Development Goals (SDGs) call for. To broaden this understanding, this study aimed to engage with student leaders of a grassroots, student-led initiative at the University of Calgary, the Sustainable Development Goals Alliance (SDGA), to better understand the experience of students who took on leadership roles in organizing SDG engagement activities. Design/methodology/approach A qualitative thematic analysis was used to understand the experiences of 12 student leaders involved in SDG programming. Semi-structured interviews asked participants to reflect on their key learnings, skills development and overall student’s experiences of leaders involved in SDG programming. Thematic analysis was applied to determine emerging themes. Findings Analyses showed that taking a leadership role in the SDGA empowered students to deepen their engagement with the SDGs and overcome barriers such as lack of knowledge and feelings of powerlessness. Secondary findings showed that community-building, flexibility and a sense of ownership were key strengths of the program and contributed toward student leaders’ feelings of hopefulness, self-confidence and inspiration. Originality/value This work offers a window into the experiences of student leaders who have worked to advance SDG engagement within their institution. Our findings suggest that student-led initiatives represent untapped potential for HEIs to prioritize and support to help deliver on their SDG implementation and engagement efforts. As HEIs offer a vital space for innovation, policy and capacity building towards implementation of the SDGs, this work demonstrates how student leadership can yield grassroots influence on HEI commitments and responses to the needs of students.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".