RSS Parrot

(Human) Attention Is (Still) All You Need: Human Oversight Makes Ai-Assisted Social Science

https://d.repec.org/n?u=RePEc:cam:camdae:2643&r=&r=cmp

Published: June 12, 2026 00:00

Large language models (LLMs) are increasingly delegated tasks once reserved for trained researchers: generating hypotheses, choosing specifications, drafting conclusions. Whether this delegation produces trustworthy science is not solely a technical…

Model Diversity Over Model Size: Unanimous LLM Ensembles Correct Over-Classification in Survey Coding

https://d.repec.org/n?u=RePEc:osf:socarx:er6mz_v1&r=&r=cmp

Published: June 5, 2026 00:00

Large language models are increasingly used to classify open-ended survey responses, but they systematically over-classify, assigning categories too liberally on ambiguous cases and producing high sensitivity but low precision. This problem is most severe…

High Agreement, Different Stories: How LLM Classifiers Reshape Demographic Patterns in Survey Data

https://d.repec.org/n?u=RePEc:osf:socarx:85kyd_v1&r=&r=cmp

Published: June 3, 2026 00:00

What we learn from open-ended survey data depends on who—or what—does the coding. Large Language Models (LLMs) promise to democratize qualitative analysis, but do high agreement rates translate into equivalent thematic findings? This study compares eight…

Large Language Models for Statistical Analysis: Can they Replace Domain-Specific Software Packages?

https://d.repec.org/n?u=RePEc:osf:metaar:zj5pc_v1&r=&r=cmp

Published: May 21, 2026 00:00

Large language models (LLMs) represent one of the most significant advances in artificial intelligence in recent decades. Although LLMs are widely used to generate statistical code in domain-specific languages such as R, Python, and SAS, they are…

Following the Crowd: Literature Support and the Capabilities of Autonomous Research Agents

https://d.repec.org/n?u=RePEc:gii:giihei:heidwp14-2026&r=&r=cmp

Published: May 18, 2026 00:00

Machine-learning models often perform poorly when asked to generalize beyond the support of the training distribution. This paper asks whether the same limitation shapes the research capabilities of autonomous large language model (LLM) agents: do they…

Automating Evidence Synthesis: A Comparative Evaluation of Large Language Models for Data Extraction

https://d.repec.org/n?u=RePEc:ris:adbewp:022484&r=&r=cmp

Published: May 15, 2026 00:00

Systematic reviews and meta-analyses (SRMAs) are important tools for evidence synthesis but have historically required substantial manual effort, particularly during the data extraction phase. To address this bottleneck, we developed and evaluated an…

When LLM Signals Hurt: A Coverage-Density Analysis of LLM-Augmented Reinforcement Learning for Stock Trading

https://d.repec.org/n?u=RePEc:osf:socarx:nxvdp_v1&r=&r=cmp

Published: May 14, 2026 00:00

We evaluate LLM-augmented reinforcement learning for stock trading on Nasdaq- 100 (2019–2023) and report a previously unmeasured experimental phenomenon: the relationship between LLM signal coverage density and trading performance is non-monotonic, with a…

LLM + Neutrosophic fsQCA: Inter-Narrative Causal Consistency and Paraconsistent Detection in Media Accounts of Urban Violence in Guayaquil

https://d.repec.org/n?u=RePEc:osf:socarx:yxdsb_v1&r=&r=cmp

Published: May 7, 2026 00:00

Urban violence in Guayaquil, Ecuador has reached crisis levels, yet causal explanations remain fragmented across local and international media. This paper introduces N-fsQCA, a Neutrosophic extension of fuzzy-set Qualitative Comparative Analysis (fsQCA),…

Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting

https://d.repec.org/n?u=RePEc:hal:journl:hal-05562231&r=&r=cmp

Published: May 1, 2026 00:00

This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price forecasting in the German, French, and Norwegian markets. Regimes are inferred via a disentangled sticky hierarchical Dirichlet process hidden…

Using large language models as a source of human behavioral data in social science experiments

https://d.repec.org/n?u=RePEc:osf:socarx:y74mu_v1&r=&r=cmp

Published: April 3, 2026 00:00

Large language models (LLMs) have prompted proposals to replace human subjects in social science experiments with simulated responses. Empirical evaluations suggest that this practice---often called silicon sampling---can sometimes approximate human…

Machine Learning Approaches for Improving Demand Forecasting Accuracy in Retail Supply Chains

https://d.repec.org/n?u=RePEc:osf:socarx:4z9be_v1&r=&r=cmp

Published: April 3, 2026 00:00

Accurate demand forecasting remains one of the most critical yet persistently challenging functions in retail supply chain management. Traditional statistical forecasting methods such as ARIMA and exponential smoothing have long served as industry…

Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models

https://d.repec.org/n?u=RePEc:gtr:gatrjs:gjbssr674&r=&r=cmp

Published: March 31, 2026 00:00

" Objective - As the investment environment improves, individuals are increasingly eager to invest their idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on…