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Record W4396838998 · doi:10.15402/esj.v9i2.70820

Service-Learning as the Violence of Mercy after the 2010 Haitian Earthquake

2023· article· en· W4396838998 on OpenAlexvenueno aff
Megan Bailey

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
FundersMcKnight Foundation
KeywordsMedical emergencySeismologyForensic engineeringPsychologyMedicineEngineeringGeology

Abstract

fetched live from OpenAlex

This article draws on La Paperson’s (2010) notion of the “violence of mercy” to demonstrate how the service-learning response to the 2010 Haitian earthquake fits into an ongoing neocolonial mission that harms communities served (p. 25). This is done by investigating the best practices of the Haiti Compact, a cohort of American colleges and universities operating service-learning initiatives in Haiti after the earthquake in thoughtful ways intending to help with the recovery efforts while meaningfully contributing service via the ritual presence of international volunteers. Despite good intentions, their material practices of service-learning risk harm for community partners and fail to meet established best practice goals. By examining the work of service-learning educators who commit to best practices, place ethics, community, and social justice, we can understand the limits of possibility for a pedagogy that is predicated upon students entering communities as outsiders intent upon meeting community needs.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0160.015
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.126
GPT teacher head0.393
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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