Building sustainability research competencies through scaffolded pathways for undergraduate research experience
Bibliographic record
Abstract
Addressing complex socio-ecological challenges, from climate change to biodiversity loss, requires collaborative co-creation and application of knowledge that bridges disciplines and diverse research communities. New models of research training are needed that emphasize these competencies and are inclusive of students from underrepresented groups in academia. This article presents learnings from a 2-year pilot project at the University of British Columbia in which we created a new course-based undergraduate interdisciplinary research experience in socio-ecological systems designed to address these twin problems. We evaluated the linkages between pedagogical design, achievement of sustainability research competencies, and overcoming barriers to research participation. We find that mentored and scaffolded learning-by-doing supported by peer group-based learning was successful in catalyzing transformative interdisciplinary learning for students. Our results emphasize the importance of scaffolding at multiple levels to remove barriers to accessing a first research experience and providing an introductory opportunity for students to build research self-efficacy and better equip students for independent research. Shifting toward pedagogies that build sustainability-related competencies and that remove barriers to access is high-reward and thus requires institutional support and investment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".