Bibliographic record
Abstract
Existing research on international service-learning (ISL) only implicitly alludes to emotions or considers emotions as a limited vehicle through which the more important work of learning occurs. This study set out to shift this focus on emotions to show how emotions are an integral part of the overall ISL experience. The aim was to understand how an ISL internship was an emotional experience for the student participants through the lens of noncognitive process theory of emotions. This was a qualitative case study of the experiences of 10 university students who engaged in an ISL internship in East Africa. Data collection instruments included preinternship surveys and emotional mind maps, postinternship surveys, and interviews. The study demonstrated that ISL can be a highly charged, emotional experience for student participants. The author argues that emotional responses are not simply a limited catalyst through which learning and transformation transpires, but constitutes forms of understanding in and of themselves. This points to the need for ISL researchers and practitioners to shift their preconceptions about the value of emotions in learning and transformation processes and attend to the emotional dimensions of ISL in their research and the implementation of these programs.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".