Advancing Equity in STEM: A Critical Analysis of NSF's Division for Equity and Excellence in STEM through Theoretical Lenses

• URL: http://arxiv.org/abs/2511.15217v1
• Date: Wed, 19 Nov 2025 08:08:22 GMT
• Title: Advancing Equity in STEM: A Critical Analysis of NSF's Division for Equity and Excellence in STEM through Theoretical Lenses
• Authors: Shaouna Lodhi,
• Abstract summary: The study finds current policies inadequate for dismantling systemic barriers.<n>The paper concludes by advocating transformative reforms that move beyond access to fundamentally restructure STEM education environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper critically analyzes the National Science Foundation's Division of Equity for Excellence in STEM. While supporting its mission to broaden participation for underrepresented groups, the study finds current policies inadequate for dismantling systemic barriers. Using Critical Race Theory and Mills's Racial Contract, the analysis reveals how well-intentioned initiatives may reinforce racial hierarchies through commodification and exclusion. The research argues that diversity efforts focused on competitiveness often fail to affirm marginalized students' full personhood and intellectual capabilities. The paper concludes by advocating transformative reforms that move beyond access to fundamentally restructure STEM education environments, aligning NSF's practices with its equity and inclusion commitments.

Related papers

• Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science [70.3658845234978]
  Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS)<n>Despite this rapid progress, the field still relies heavily on empirical trial-and-error.<n>This bottleneck stems from the ambiguity of attribution.<n>We propose a factor attribution paradigm to systematically identify collaboration-driving factors.
  arXiv  Detail & Related papers  (2026-02-05T04:19:52Z)
• Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation [71.86087908416255]
  We introduce a payoff allocation framework based on the least core (LC) concept.<n>Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction.<n>Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances.
  arXiv  Detail & Related papers  (2026-02-03T11:10:50Z)
• STEMVerse: A Dual-Axis Diagnostic Framework for STEM Reasoning in Large Language Models [14.280808299733868]
  We propose a diagnostic framework designed to analyze the reasoning capabilities of Large Language Models (LLMs)<n>We re-aggregate over 20,000 STEM problems from mainstream benchmarks into a unified "Discipline $times$ Cognition" capability space.<n>Our empirical results reveal structural failure patterns in STEM reasoning.
  arXiv  Detail & Related papers  (2026-01-14T07:17:12Z)
• A Feminist Account of Intersectional Algorithmic Fairness [0.0]
  We propose Substantive Intersectional Algorithmic Fairness, extending Green's notion of substantive algorithmic fairness with insights from intersectional feminist theory.<n>We introduce ten desiderata within the ROOF methodology to guide the design, assessment, and deployment of algorithmic systems.<n>By bridging computational and social science perspectives, we provide actionable guidance for more equitable, inclusive, and context-sensitive intersectional algorithmic practices.
  arXiv  Detail & Related papers  (2025-08-25T12:09:04Z)
• MIST: Towards Multi-dimensional Implicit BiaS Evaluation of LLMs via Theory of Mind [27.209638457499427]
  Theory of Mind (ToM) in Large Language Models (LLMs) refers to their capacity for reasoning about mental states.<n>We propose an evaluation framework that leverages the Stereotype Content Model (SCM) to reconceptualize bias as a multi-dimensional failure in ToM across Competence, Sociability, and Morality.
  arXiv  Detail & Related papers  (2025-06-17T03:50:57Z)
• Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability [0.9065034043031668]
  The thesis addresses the need for equity and transparency in NLP systems. It introduces an innovative algorithm to mitigate biases in high-risk NLP applications. It also presents a model-agnostic explainability method that identifies and ranks concepts in Transformer models.
  arXiv  Detail & Related papers  (2024-10-16T12:38:58Z)
• Social Debiasing for Fair Multi-modal LLMs [59.61512883471714]
  Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities.<n>These models often inherit deep-rooted social biases from their training data, leading to uncomfortable responses with respect to attributes such as race and gender.<n>This paper addresses the issue of social biases in MLLMs by introducing a comprehensive counterfactual dataset with multiple social concepts.
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity [0.0]
  This paper documents the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.
  arXiv  Detail & Related papers  (2024-01-08T21:10:18Z)
• MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
  We introduce a novel evaluation paradigm for Large Language Models (LLMs) This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
  arXiv  Detail & Related papers  (2023-12-28T15:49:43Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• 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)
• Fair Machine Learning in Healthcare: A Review [90.22219142430146]
  We analyze the intersection of fairness in machine learning and healthcare disparities. We provide a critical review of the associated fairness metrics from a machine learning standpoint. We propose several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.
  arXiv  Detail & Related papers  (2022-06-29T04:32:10Z)
• Western ideological homogeneity in entrepreneurial finance research: Evidence from highly cited publications [0.0]
  Entrepreneurial finance discipline is born to explore the connection between finance and entrepreneurship. Despite the global presence of entrepreneurship, the literature of entrepreneurial finance is suspected to be Western ideologically homogenous.
  arXiv  Detail & Related papers  (2020-07-31T18:03:44Z)

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.