Bibliographic record
Abstract
International Service Learning: Engaging host communities by Marianne A. Larsen New York, New York: Routledge, 2016, 287 pages ISBN: 978-1-138-84131-4 (hardcover) Service learning is a growing field of study and practice in a number of Canadian universities. It involves concept of combining travel with some sort of community project, usually in a majority world nation. The use of terms minority world for Western and majority world for those that are usually called developing nations by Western is one of strongest points made in text to address how language and theory foster and maintain positions of privilege. Service learning has traditionally aimed to minority world youth's skills and empathy as they a community improve. However, as a number of edited chapters in Larsen's book illustrate, learning is problematic. We can connect its desire for service in interests of helping those poor others with colonial legacies of Europeans going to other to help develop them. Many of chapter authors describe a number of shortcomings with learning projects including building community infrastructures, such as libraries, that local communities do not want or cannot use, taking jobs away from locals, exposing locals to privileged white kids whose attitudes and actions can damage locals' self esteem, and increasing community dependence and beliefs that West is right model of development for other to follow. Larsen et al. (2016) demonstrate awareness of these shortcomings and thus aim to write book from perspectives of those who are recipients of projects. Most of chapters present research carried out with local community people in majority nations such as Ghana, Uganda, Jamaica, or Nicaragua, or they describe perspectives of bridging agencies who help to facilitate projects. The concerns raised in this book's chapters can be summed up by chapter author Jessica Arends' comment that, the data reveals that international learning interactions occur at a complex nexus of expectations regarding race, gender and privilege, leading to feelings of exploitation, entitlement and stereotyping (p. 109-110). This book should be required reading for any university administrator, counselor, or individual who engages in learning. It turns much needed attention on how receivers of learning projects perceive them. Even though some of interviewees may give overly positive answers due to their fear of losing program benefits and money, it is clear that there are number of significant issues with these projects. …
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".