Strengthening sustainability leadership competencies through university internships
Bibliographic record
Abstract
Purpose This paper aims to determine whether high school students can become agents of change in their local communities by participating in a formal internship program implemented through a partnership between academia (high schools and universities), nonprofit organizations and key community stakeholders. Design/methodology/approach Pre- and post-assessments, activity journals and on-line interviews are used to measure the impact of internships on high school students’ sustainability leadership, using a train-the-trainer intervention led by university interns. A conceptual problem-solving framework is proposed and empirically tested to explore the linkages between complex problem constellations, sustainability transition strategies and sustainability visions. Findings The five core leadership competencies (systems thinking, strategic, anticipatory, normative and interpersonal) may not be as uniquely discrete as suggested in the literature. An effective learning experience depends on students’ developing competence in their ability to implement a strategic intervention, which is better acquired through hands-on experience rather than a classroom setting. Practical implications Students need experiential learning outside of the classroom to make sustainability come alive as a viable option for their communities. Social implications The principles of social responsivity, engagement, experiential learning, capacity-building and entrepreneurialism can be executed by transforming the campus into a learning lab, which includes the local community. Originality/value This study empirically demonstrates that students need involvement in strategic interventions to imagine and conceptualize sustainability visions. It also shows how academia can help fulfill the United Nations sustainable development goals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".