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Record W4365999179 · doi:10.1145/3579455

A Matter of Time: Anticipation Work and Digital Temporalities in Refugee Humanitarian Assistance in Turkey

2023· article· en· W4365999179 on OpenAlexaff
Cansu Ekmekcioglu, Priyank Chandra, Syed Ishtiaque Ahmed

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRefugeeTemporalityTemporalitiesAnticipation (artificial intelligence)Work (physics)SociologyScholarshipSituatedResource (disambiguation)Computer-supported cooperative workPolitical scienceComputer scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

Scholarly work interrogating time and temporality in CSCW predominantly focuses on the temporal coordination of work in high-resource settings and is usually based in Global North. This paper aims to complicate and complement this scholarship by investigating the temporal entanglements of digital humanitarian work with refugees and asylum seekers in Turkey during COVID-19. We interviewed 22 humanitarian workers to understand their experiences and concerns as well as strategies they employed to support refugees and immigrants at a distance. The data reveal the complex temporal, informational, and infrastructural dimensions of technologically-mediated refugee support work, challenging the trope of "pivot to remote work", as popular in western countries. Our findings contribute to the CSCW research on the theory of anticipation work and its relationship with the concept of collaborative rhythms to explicate the relational and situated aspects of the temporal experiences of humanitarian workers in low-resource settings.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.007
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0010.001
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 head0.064
GPT teacher head0.374
Teacher spread0.310 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ACM on Human-Computer InteractionSame topicSocial Work Education and PracticeFrench-language works237,207