Committing to refugee resettlement volunteering: Attaching, detaching and displacing organizational ties
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".