From "knowing" to "not knowing": Critical global citizenship education for engineering partnerships
Bibliographic record
Abstract
This article was part of a larger study that explored community participants’ perspectives in [Municipality, Country] about the long-term global service learning (GSL) partnership with [Name of university] University’s College of Engineering (Author, year). This article explores the question: From the community participants’ perspectives, what are their educational goals for the university engineering students in this partnership? While I intentionally centered this article on the community participants’ perspectives, I also explored areas of alignment and areas of difference between the different stakeholder groups’ perspectives about learning and knowledge. Although global citizenship surfaced in interviews with both community and university participants, the community participant perspectives push farther than the university administrators/ faculty and call for critical global citizenship education (Andreotti, 2006).
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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 | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.092 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".