A computational model for gender asset gap management with a focus on gender disparity in land acquisition and land tenure security

• URL: http://arxiv.org/abs/2404.09164v1
• Date: Sun, 14 Apr 2024 06:59:25 GMT
• Title: A computational model for gender asset gap management with a focus on gender disparity in land acquisition and land tenure security
• Authors: Oluwatosin Ogundare, Lewis Njualem,
• Abstract summary: Land acquisition and land tenure security are complex issues that affect various cultural groups differently. The proposed framework aims to fill this gap by incorporating cultural and policy factors in developing a new measurement framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gender inequality is a significant concern in many cultures, as women face significant barriers to asset acquisition particularly land ownership and control. Land acquisition and land tenure security are complex issues that affect various cultural groups differently, leading to disparities in access and ownership especially when superimposed with other socio-economic issues like gender inequality. Measuring the severity of these issues across different cultural groups is challenging due to variations in cultural norms, expectations and effectiveness of the measurement framework to correctly assess the level of severity. While nominal measures of gender asset gap provide valuable insights into land acquisition and tenure security issues, they do not fully capture the nuances of cultural differences and the impact of governmental and corporate policies that influence gender disparity in land ownership and control. The proposed framework aims to fill this gap by incorporating cultural and policy factors in developing a new measurement framework equipped with a more robust, comprehensive metric to standardize the approach to assessing the severity of gender asset disparity in a general sense but with a focus on land acquisition and tenure security to engender more effective interventions and policy recommendations.

Related papers

• Gender assignment in doctoral theses: revisiting Teseo with a method based on cultural consensus theory [0.0]
  This study critically evaluates gender assignment methods within academic contexts. The research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database.
  arXiv  Detail & Related papers  (2025-01-20T15:22:01Z)
• The Femininomenon of Inequality: A Data-Driven Analysis and Cluster Profiling in Indonesia [0.0]
  This study examines regional disparities in gender empowerment and inequality through the Gender Empowerment Index (IDG) and Gender Inequality Index (IKG) Despite Indonesia's economic growth and incremental progress in gender equality, substantial regional differences remain. The analysis reveals that local socio-economic conditions and governance frameworks play a critical role in shaping regional gender dynamics.
  arXiv  Detail & Related papers  (2024-11-13T23:45:58Z)
• Properties of fairness measures in the context of varying class imbalance and protected group ratios [15.942660279740727]
  We study the general properties of fairness measures for changing class and protected group proportions. We also measure how the probability of achieving perfect fairness changes for varying class imbalance ratios. Our results show that measures such as Equal Opportunity and Positive Predictive Parity are more sensitive to changes in class imbalance than Accuracy Equality.
  arXiv  Detail & Related papers  (2024-11-13T08:18:03Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [58.130894823145205]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning. We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
  We focus on extrinsic evaluation of cultural competence in two text generation tasks. We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
  arXiv  Detail & Related papers  (2024-06-17T14:03:27Z)
• Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates [4.857640117519813]
  We argue that cultural and psychological factors play a vital role in the cognitive processing of offensiveness. We demonstrate substantial cross-cultural differences in perceptions of offensiveness. Individual moral values play a crucial role in shaping these variations.
  arXiv  Detail & Related papers  (2023-12-11T22:12:20Z)
• Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity [5.333582981327497]
  The authors demonstrate the impact of such algorithmic bias in the microfinance context. We find that in addition to legally protected characteristics, sensitive attributes such as single parent status and number of children can result in imbalanced harm.
  arXiv  Detail & Related papers  (2023-08-01T10:34:26Z)
• Social Diversity Reduces the Complexity and Cost of Fostering Fairness [63.70639083665108]
  We investigate the effects of interference mechanisms which assume incomplete information and flexible standards of fairness. We quantify the role of diversity and show how it reduces the need for information gathering. Our results indicate that diversity changes and opens up novel mechanisms available to institutions wishing to promote fairness.
  arXiv  Detail & Related papers  (2022-11-18T21:58:35Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.