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Analyzing the Social and Cultural Determinants of Keeping and Using Firearms in Khuzestan Province

2023· article· en· W6963650201 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsnot available
Fundersnot available
KeywordsPossession (linguistics)Qualitative researchQualitative propertyData collectionQualitative analysisTrustworthinessUnder-reportingPoison control

Abstract

fetched live from OpenAlex

Introduction In many countries, including America, Canada, South Africa, Brazil, Colombia, and certain European nations, firearm-related injuries are a significant contributor to the overall number of deaths (Dahlberg, 2004). The majority of these incidents occur in countries where civilian firearm possession is unrestricted (Singh, 2005). According to a report from the United Nations, South Africa has the second highest rate of firearm-related deaths globally (Sanaei, 2004). In contrast, Iran has relatively few reported firearm injuries with the most recent official statistics from 2014 indicating over one million illegal firearms and a similar number of authorized firearms in circulation. Materials & Methods This study employed an interpretive approach to explore and elucidate participants' experiences and interpretations of firearm use by using qualitative methods. Specifically, the foundational data theory method was utilized among various qualitative research methods to conduct and present the research. Criterion sampling was employed to select all cases that met specific researcher-defined criteria. The participants included senior experts from the police deputy of the governorate, social deputy of the governorate, senior experts of the governorate, individuals arrested in this field, senior experts of the police force, academic experts, and citizens of Khuzestan Province (Ahvaz, Abadan, Izeh, and Shadgan cities). In-depth interviews were the primary technique used for data collection. Data analysis followed the systematic approach of contextual theory, employing open, axial, and selective coding methods. Common validation techniques were utilized to ensure the scientific rigor and trustworthiness of the research. Discussion of Results & Conclusion The data analysis revealed various factors within the research model. Causal conditions encompassed the quality and lack of official supervision, civil disobedience, symbolic function of weapons, feelings of deprivation and discrimination, demonstrative use of weapons, tribal power dynamics, strategic planning weaknesses, and anomic conditions. Additionally, tribal prejudice, identity reevaluation, identity-driven use of weapons, and background conditions (economic determinants), such as poverty, unemployment, perceived economic pressure, economic-social base of users, easy access to weapons, and generation of income through unauthorized weapon sales were identified. The intervening conditions (cultural determinants) included the influence of nomadic culture, patriarchal cultural stagnation, weapons as symbols of nobility and leadership honor, low cultural capital of users, delayed ethnic customs and traditions, lack of family socialization, inactive cultural organizations, lack of free time, ethnic reference groups, belief in honoring local traditions, and value of using weapons. The proposed strategies involved the necessity of enacting laws, regulating licensed weapons, continuous monitoring of weapon licenses, citizen-centered monitoring, developing cultural strategies to replace weapons, enhancing the efficiency of laws in local conflict resolution, and utilizing local trustees' capacity. The participants highlighted the significant consequences, including heightened insecurity, negative evaluation of local governance performance by citizens, and reduced sense of belonging, all revolving around the core category of "unbalanced reproduction of ethnic traditions."

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.501
GPT teacher head0.648
Teacher spread0.147 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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