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Record W965256093 · doi:10.18778/1733-8069.8.3.04

Family Changes in Iranian Kurdistan: A Mixed Methods Study of Mangor and Gawerk Tribes

2012· article· en· W965256093 on OpenAlexaff
Ahmad Mohammadpur, Juliet Corbin, Rasoul Sadeghi, Mehdi Rezaei

Bibliographic record

VenuePrzegląd Socjologii Jakościowej · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKinshipModernization theoryQualitative propertyQualitative researchSample (material)Government (linguistics)Quantitative researchSociologyUrbanizationGeographySocioeconomicsGenealogySocial scienceEconomic growthAnthropologyHistory

Abstract

fetched live from OpenAlex

Over the last few decades, the Iranian Kurdish society, including family and kinship systems, has experienced enormous changes as a result of government implemented modernization efforts. This paper reports the results of a quantitative/ qualitative mixed methods study aimed at exploring (a) the nature of change in family and kinship systems and (b) how people understand and interpret these changes. The sample for this study was drawn from the Mangor and Gawerk tribes residing in the Mahabad Township located in the West Azerbaijan Province of Iran. Using standardized questionnaires, 586 people were sampled as part of the quantitative portion of the study. For the qualitative portion, data was collected on 20 people using both in-depth interviews and participant observations. The quantitative data was analyzed by SPSS software and the qualitative data was interpreted using grounded theory procedures. The quantitative findings showed that the urbanization, modern education, and mass media have all contributed to the emergence of a new form of family and kinship life. In addition, while supporting quantitative findings, the qualitative results revealed that participants were aware of and sensitive to sources, processes, and effects of modernization on their family and kinship life.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.422
Teacher spread0.356 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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