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Record W4392545986 · doi:10.32920/25360846.v1

Client or Volunteer? Understanding Neoliberalism and Neocolonialism Within International Volunteer Health Work

2024· preprint· en· W4392545986 on OpenAlexafffundabout
Oona St-Amant, Catherine Ward‐Griffin, Hélène Berman, Arja Vainio-Mattila

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsNipissing UniversityWestern UniversityToronto Metropolitan University
FundersCanadian Institutes of Health Research
KeywordsNeocolonialismNeoliberalism (international relations)Volunteer workVolunteerWork (physics)SociologyPolitical sciencePublic relationsSocial scienceLaw

Abstract

fetched live from OpenAlex

As international volunteer health work increases globally, research pertaining to the social organizations that coordinate the volunteer experience in the Global South has severely lagged. The purpose of this ethnographic study was to critically examine the social organizations within Canadian NGOs in the provision of health work in Tanzania. Multiple, concurrent data collection methods, including text analysis, participant observation and in-depth interviews were utilized. Data collection occurred in Tanzania and Canada. Neoliberalism and neocolonialism were pervasive in international volunteer health work. In this study, the social relations—“volunteer as client,” “experience as commodity,” and “free market evaluation”—coordinated the volunteer experience, whereby the volunteers became “the client” over the local community and resulting in an asymmetrical relationship. These findings illuminate the need to generate additional awareness and response related to social inequities embedded in international volunteer health work.

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)0.009
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.028
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.069
GPT teacher head0.348
Teacher spread0.279 · 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.

Study designQualitative
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

Citations0
Published2024
Admission routes3
Has abstractyes

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