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Record W4285153283 · doi:10.1525/sod.2021.0039

“Just Push It Through”

2022· article· en· W4285153283 on OpenAlexaboutno aff
Sophia Boutilier

Bibliographic record

VenueSociology of Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityPrivilege (computing)RedressAgency (philosophy)Public relationsPoliticsPolitical scienceFeelingFace (sociological concept)Social psychologySociologyPsychologyLawSocial science

Abstract

fetched live from OpenAlex

In what ways, if any, do development workers practice solidarity? In-depth interviews with 42 current and former workers for the Canadian federal development agency reveal that emotions are important factors in how solidarity is enacted and where it breaks down. Almost all the interviewees described feelings of frustration and reward in their development work, but whether these emotions contribute solidarity is contingent on the extent to which these workers identify with their partners. The more they identify, the more they push for the development outcomes they believe will best serve their partners, often despite Canadian political priorities. However, the conflict between Canadian and development interests can lead to burnout, especially for women, who are more likely to challenge the organization—and to face professional hurdles as a result. In contrast, workers who see themselves as primarily accountable to Canadians experience less frustration and easier career paths. For this group, the reward of “doing good” becomes an additional source of privilege that further separates them from development partners. The case of Canadian development workers highlights the challenges of solidarity as an elusive yet important development ethic and sheds light on broader questions of how solidarity can challenge privilege to redress inequalities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.320
Teacher spread0.276 · 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 teacher head, 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

Citations2
Published2022
Admission routes1
Has abstractyes

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