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Record W4392242308 · doi:10.5206/ijoh.2023.3.15957

Homelessness and Gender Inequality in the Middle East and North Africa Region

2024· article· en· W4392242308 on OpenAlexaffvenue
Negar Shahriariyanehsari, Fawziah Rabiah-Mohammed, Abe Oudshoorn, Alex Nelson

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork UniversityWestern University
Fundersnot available
KeywordsPhenomenonArgument (complex analysis)InequalityMiddle EastPolitical scienceGender equalityGender studiesDevelopment economicsPublishingGender inequalitySociologyEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

While homelessness is a global phenomenon, research on homelessness skews heavily towards high-income nations such as those in North America, Europe, and Australia. The academic publishing sector also highly privileges articles written in English. Therefore, homelessness in the global south or non-English as a first language countries is less understood and less recognized. In this article, we explore homelessness as a phenomenon in the Middle East and North African (MENA) region. In particular, we use a gender-based analysis to understand the highly gendered nature of homelessness in this region, presenting an argument that while homelessness is gendered globally, in regions where gender inequality is greater, women and gender-diverse people will be more disproportionately impacted by homelessness. Therefore, obtaining Sustainable Development Goal 5 of ‘Gender Equality’ and efforts to prevent homelessness will go hand-in-hand within the MENA region.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.229
GPT teacher head0.401
Teacher spread0.172 · 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 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
Published2024
Admission routes2
Has abstractyes

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