惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

C
Check Point Blog
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
D
Docker
腾讯CDC
The GitHub Blog
The GitHub Blog
大猫的无限游戏
大猫的无限游戏
Microsoft Security Blog
Microsoft Security Blog
GbyAI
GbyAI
Stack Overflow Blog
Stack Overflow Blog
博客园 - 司徒正美
T
The Blog of Author Tim Ferriss
Vercel News
Vercel News
P
Proofpoint News Feed
雷峰网
雷峰网
博客园_首页
B
Blog RSS Feed
Microsoft Azure Blog
Microsoft Azure Blog
爱范儿
爱范儿
V
V2EX
F
Fortinet All Blogs
酷 壳 – CoolShell
酷 壳 – CoolShell
MyScale Blog
MyScale Blog
S
SegmentFault 最新的问题

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Gender issues in fundamental physics: Strumia's bibliomet...
Philip Ball, T. Benjamin Britton, Erin Hengel, Philip Moriarty, · 2020-12-03 · via cs.SI updates on arXiv.org

Alessandro Strumia recently published a survey of gender differences in publications and citations in high-energy physics (HEP). In addition to providing full access to the data, code, and methodology, Strumia (2020) systematically describes and accounts for gender differences in HEP citation networks. His analysis points both to ongoing difficulties in attracting women to high-energy physics and an encouraging-though slow-trend in improvement. Unfortunately, however, the time and effort Strumia (2020) devoted to collating and quantifying the data are not matched by a similar rigour in interpreting the results. To support his conclusions, he selectively cites available literature and fails to adequately adjust for a range of confounding factors. For example, his analyses do not consider how unobserved factors -- e.g., a tendency to overcite well-known authors -- drive a wedge between quality and citations and correlate with author gender. He also fails to take into account many structural and non-structural factors -- including, but not limited to, direct discrimination and the expectations women form (and actions they take) in response to it -- that undoubtedly lead to gender differences in productivity. We therefore believe that a number of Strumia's conclusions are not supported by his analysis. Indeed, we re-analyse a subsample of solo-authored papers from his data, adjusting for year and journal of publication, authors' research age and their lifetime "fame". Our re-analysis suggests that female-authored papers are actually cited more than male-authored papers. This finding is inconsistent with the "greater male variability" hypothesis Strumia (2020) proposes to explain many of his results.