MétaCan
Menu
Back to cohort
Record W2779686217 · doi:10.1111/1468-4446.12335

Talking about time: temporality and motivation for international <scp>C</scp>hristian humanitarian actors in <scp>S</scp>outh <scp>S</scp>udan

2017· article· en· W2779686217 on OpenAlexafffund
Amy Kaler, John R. Parkins

Bibliographic record

VenueBritish Journal of Sociology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsTemporalityFaithSociologyWork (physics)NarrativeConstruct (python library)EpistemologyEngineeringComputer science

Abstract

fetched live from OpenAlex

We investigate ways in which international evangelical Christian humanitarians talk about time as they engaged in humanitarian assistance and development work in South Sudan. Our focus on Christian development work is motivated by a desire to understand how and why people persevere in humanitarian work and reconcile seemingly impossible circumstances and to further elaborate sociological conceptions of time as experienced by people in their own lives. We argue that their faith commitments produce ways of understanding time and causality which make possible their attachment to risky and dangerous work. Our work is based on in-depth interviews with people who work or have recently worked for Christian faith-based organizations in South Sudan (n = 30). Drawing on Tavory and Eliasoph's () concepts of life narratives and life projects, we treat our participants as culturally competent actors who skilfully construct their stories through drawing on collectively shared faith-inflected ideas about temporality and causation. We argue that these ideas represent an important resource for getting through the risks, challenges and uncertainties of doing humanitarian work in complex crises.

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)0.003
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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 designObservational
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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueBritish Journal of SociologySame topicReligion, Society, and DevelopmentFrench-language works237,207