A Habitat for Humanity and University Partnership: Enhancing on International Experiential Learning in El Salvador
Bibliographic record
Abstract
The increase in international experiential learning (IEL hereafter) opportunities being developed by universities in the global north requires more attention both generally and with regards to specific IEL programming objectives. This paper provides observations and assessment of a case study of university-student participation in home-builds in El Salvador over the course of three years – a partnership between Habitat for Humanity and a Canadian university. The information collected is assessed relative to the key critiques and to the recommendations advocated in the IEL literature, with the intention of incorporating these critiques and recommendations in future IEL planning for this partnership, and to inform IEL work more generally. The key observations and recommendations include the need for enhanced student preparation pre and post-trip – meaning ‘critical reflection’ processes and materials on privilege and personal goals themes; on specific global south context; and enhancing on intercultural learning and awareness activities and processes e.g., more closely integrated host-community and participant relationship-building opportunities. These recommendations are seen as important for enhancing on this specific IEL program and its short-duration time-frame, while suggesting useful guideposts for IEL more generally, as its occurrence increases within the university setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".