arXiv subjects

Zia Qi

Publications and source records attributed to Zia Qi.

7 recordsLinked to original sources

arXiv2026

AI-Assisted Curation of Conference Scholarship: Compiling, Structuring, and Analyzing Two Decades of Presentations at the Society for Social Work and Research

Purpose: This study developed a comprehensive database of presentation abstracts from the Society for Social Work and Research (SSWR) Annual Conference and examined patterns in research methodology, authorship, collaboration, and institutional participation over two decades. Method: Abstract metadata was compiled from the SSWR Confex conference management system for presentations from 2005 to 2026 using web scraping. A small language model (gpt-oss:20b) performed classification and extraction tasks on abstracts, including categorization of methodologies and parsing of author affiliations, with human review at each major stage to ensure accuracy. Results: The database contains 23,793 presentations with 69,924 author records representing 20,779 unique researchers from 4,049 institutions across 93 countries. Annual conference presentations increased from 423 in 2005 to 1,935 in 2026, representing a compound annual growth rate of 7.5%. Quantitative methods predominated (61.1%), followed by qualitative approaches (23.4%), mixed methods (9.1%), and reviews (5.4%). The mean number of authors per presentation increased from 2.22 in 2005 to 3.31 in 2026. International participation grew from 4.5% to 13.5% of author affiliations over the observation period. Discussion: Findings indicate substantial growth in SSWR conference participation, alongside increased collaboration and international engagement. The methodological distribution reveals continued quantitative predominance with growing qualitative representation. This database provides research infrastructure for systematic hypothesis testing about research priorities and disciplinary development over time, enabling analyses that inform both scholarship and conference planning.

cs.DL↗

arXiv2026

Reasoning Language Models for complex assessments tasks: Evaluating parental cooperation from child protection case reports

Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks. We examined their potential to assess parental cooperation during CPS interventions using case reports, a case factor characterized by ambiguous and conflicting information. Methods: A four stage workflow comprising (1) case reports collection, (2) reasoning-based assessment of parental cooperation, (3) automated category extraction, and (4) case labeling was developed. The performance of RLMs with different parameter sizes (255B, 32B, 4B) was compared against human validated data. Two expert human reviewers (EHRs) independently classified a weighted random sample of reports. Results: The largest RLM achieved the highest accuracy (89%), outperforming the initial approach (80%). Classification accuracy was higher for mothers (93%) than for fathers (85%), and EHRs exhibited similar differences. Conclusions: RLMs' reasoning can effectively assess complex case factors such as parental cooperation. Lower accuracy in assessing fathers' cooperation supports the argument of a stronger professional focus on mothers in CPS interventions.

cs.CY↗

arXiv2026

Small Models Achieve Large Language Model Performance: Evaluating Reasoning-Enabled AI for Secure Child Welfare Research

Objective: This study develops a systematic benchmarking framework for testing whether language models can accurately identify constructs of interest in child welfare records. The objective is to assess how different model sizes and architectures perform on four validated benchmarks for classifying critical risk factors among child welfare-involved families: domestic violence, firearms, substance-related problems generally, and opioids specifically. Method: We constructed four benchmarks for identifying risk factors in child welfare investigation summaries: domestic violence, substance-related problems, firearms, and opioids (n=500 each). We evaluated seven model sizes (0.6B-32B parameters) in standard and extended reasoning modes, plus a mixture-of-experts variant. Cohen's kappa measured agreement with gold standard classifications established by human experts. Results: The benchmarking revealed a critical finding: bigger models are not better. A small 4B parameter model with extended reasoning proved most effective, outperforming models up to eight times larger. It consistently achieved "substantial" to "almost perfect" agreement across all four benchmark categories. This model achieved "almost perfect" agreement (\k{appa} = 0.93-0.96) on three benchmarks (substance-related problems, firearms, and opioids) and "substantial" agreement (\k{appa} = 0.74) on the most complex task (domestic violence). Small models with extended reasoning rivaled the largest models while being more resource-efficient. Conclusions: Small reasoning-enabled models achieve accuracy levels historically requiring larger architectures, enabling significant time and computational efficiencies. The benchmarking framework provides a method for evidence-based model selection to balance accuracy with practical resource constraints before operational deployment in social work research.

cs.CY↗

arXiv2025

Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.

cs.CY↗

arXiv2024

AI and Cultural Context: An Empirical Investigation of Large Language Models' Performance on Chinese Social Work Professional Standards

Objective: This study examines how well leading Chinese and Western large language models understand and apply Chinese social work principles, focusing on their foundational knowledge within a non-Western professional setting. We test whether the cultural context in the developing country influences model reasoning and accuracy. Method: Using a published self-study version of the Chinese National Social Work Examination (160 questions) covering jurisprudence and applied knowledge, we administered three testing conditions to eight cloud-based large language models - four Chinese and four Western. We examined their responses following official guidelines and evaluated their explanations' reasoning quality. Results: Seven models exceeded the 60-point passing threshold in both sections. Chinese models performed better in jurisprudence (median = 77.0 vs. 70.3) but slightly lower in applied knowledge (median = 65.5 vs. 67.0). Both groups showed cultural biases, particularly regarding gender equality and family dynamics. Models demonstrated strong professional terminology knowledge but struggled with culturally specific interventions. Valid reasoning in incorrect answers ranged from 16.4% to 45.0%. Conclusions: While both Chinese and Western models show foundational knowledge of Chinese social work principles, technical language proficiency does not ensure cultural competence. Chinese models demonstrate advantages in regulatory content, yet both Chinese and Western models struggle with culturally nuanced practice scenarios. These findings contribute to informing responsible AI integration into cross-cultural social work practice.

cs.CY↗

arXiv2024

A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.

cs.CL↗

arXiv2024

Demystifying Application Programming Interfaces (APIs): Unlocking the Power of Large Language Models and Other Web-based AI Services in Social Work Research

Application Programming Interfaces (APIs) are essential tools for social work researchers aiming to harness advanced technologies like Large Language Models (LLMs) and other AI services. This paper demystifies APIs and illustrates how they can enhance research methodologies. It provides an overview of API functionality and integration into research workflows, addressing common barriers for those without programming experience. The paper offers a technical breakdown of code and procedures for using APIs, focusing on connecting to LLMs and leveraging them to facilitate API connections. Practical code examples demonstrate how LLMs can generate API code for accessing specialized services, such as extracting data from unstructured text. Emphasizing data security, privacy considerations, and ethical concerns, the paper highlights the importance of careful data handling when using APIs. By equipping researchers with these tools and knowledge, the paper aims to expand the impact of social work research through the effective incorporation of AI technologies.

cs.SE↗