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Record W2755146390 · doi:10.5465/amd.2017.0040

Organizing Refugee Camps: “Respected Space” and “Listening Posts”

2017· article· en· W2755146390 on OpenAlexaff
Marlen de la Chaux, Helen Haugh, Royston Greenwood

Bibliographic record

VenueAcademy of Management Discoveries · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRefugeeSomaliActive listeningSpace (punctuation)Political sciencePublic relationsSociologySocial psychologyPsychologyLawCommunicationComputer science

Abstract

fetched live from OpenAlex

We examine an organizational form that has received little attention despite its social significance—the refugee camp. From an in-depth case study of the Dadaab refugee camp in Kenya, we explain how these organizations maintain social stability even though refugees live for decades in them and are deprived of the freedom to move or work outside the camp’s boundaries. Our analysis finds that refugee camps are characterized by a parallel organizational structure in which the institutional worlds of (primarily Western) camp officials and (in our case, primarily Somali) refugees coexist. Mutual dependence between camp officials and refugees enables the use of a respected space of reciprocal tolerance and minimal intrusion, and a listening post that is perceived as a legitimate communication arrangement and that acts as a safety valve. These complementary mechanisms provide the means by which to allay the otherwise high potential of severe discontent.Whiteboard Video AbstractAOM Video Player5783748258001

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)0.002
metaresearch head score (Gemma)0.004
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 score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.011
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

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