Inter-institutional Project on Global Education: Postsecondary Learners’ Collaborative Meaning-Making
Bibliographic record
Abstract
This study reports on educational outcomes of collaborative educational project amongst postsecondary students with institutions and majors of different types. Through a project to advocate understanding of Sustainable Development Goals (SDGs), part of United Nations goals and mandates, undergraduate students from various departments of two postsecondary institutions collaboratively worked to become a local resource for global education. Our study is anchored in Multiple Identities (Norton, 1995) of the participant students with their pluricultural competence (Coste, Moore and Zarate, 1997, 2009; Dagenais and Moore, 2004, 2008; Marshall and Moore, 2013, 2016, 2018; Moore, 2006, 2010; Moore and Gajo, 2009) as a means of enacting professional and learner identities. Implications from similar quantitative studies (Mizokami, 2009; Rose-Kransnor etal, 2006; Yamada and Mori, 2010) have been used to enhance our anecdotal analysis. Through the Narrative approach with “restorying” (Creswell, 2013), the data has been analyzed to represent the voices the participants. Findings suggest that 1) multiple identities of students uniquely inform themselves to navigate their professional skills; 2) students embrace distinctive specialities and common knowledge as learning opportunities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".