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Record W2762391818 · doi:10.1177/0018726717729209

Committing to refugee resettlement volunteering: Attaching, detaching and displacing organizational ties

2017· article· en· W2762391818 on OpenAlexaff
Kirstie McAllum

Bibliographic record

VenueHuman Relations · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRefugeeOrganizational commitmentNegotiationPublic relationsOrganizational cultureInterpersonal tiesSocial psychologyPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

As members of local host communities, volunteers play an important role in effective long-term refugee resettlement. This study investigated the nature of volunteer commitment by organizational volunteers who were assigned a front-line role in organizing material assistance and providing information about cultural practices for newly arrived refugees. Using interview data from volunteers, organizational representatives, and organizational recruitment and training documents, the study found that volunteers’ commitment was structured by the presence and absence of volunteer coordinators, the organization’s clients and volunteers’ significant others. While insufficient ties to the organization or strong, competing ties from significant others led volunteers to detach themselves from the organization, overly strong affective ties with refugees displaced organizational ties, leading to volunteers’ organizational exit. This study problematizes an individual-centric, psychological notion of commitment; instead, it situates commitment as a collective communicative process whereby relevant stakeholders negotiate the relationships that tie them together. It thus expands the range of voices present in decisions about commitment and provides new data on how organizational and relational others impact sustainable volunteer management.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.040
GPT teacher head0.373
Teacher spread0.333 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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