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Record W4393317874 · doi:10.18280/ijsdp.190309

Cultural Dynamics of Gendered Spaces: Behavioral Influences in Amman's Outdoor Areas

2024· article· en· W4393317874 on OpenAlexvenueno aff
Bushra Zalloom

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
FundersZarqa University
KeywordsDynamics (music)Behavioral patternGeographyEnvironmental planningPsychologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

This research focuses on the anthropology of gendered spaces and their connection with cultural aspects.Reviewing previous literature confirms that the cultural dynamics of gendered spaces in Jordan have not been widely researched, therefore exploring this subject will contribute to knowledge.Theorizing the interpretation of gender in urban spaces can shed light on gaps among culture and urban theories and identify some crucial concepts in the cultural dynamics of gendered spaces.This research aims at exploring the influence of cultural aspects on the behavior of middle-aged users in outdoor spaces.The research highlights the importance of understanding the cultural dynamics of gendered spaces.A comparative study is conducted to compare the landscape patterns in the old parts of Amman with the modern parts.A mixed methods approach is used to increase the accuracy of the research outcomes.The research findings confirm that understanding the users' behaviors, perceptions, and activities is important when planning and designing outdoor spaces.It also confirms the influence of cultural aspects on the behavior of individuals, especially women.The Research findings will affect government policy, professional practice, and the quality of the built environment; it will add great value to the field of gender-sensitive planning.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.418
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.347
Teacher spread0.313 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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