Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interactive Gender Inference with Integer Linear Programming
Authors: Shoushan Li, Jingjing Wang, Guodong Zhou, Hanxiao Shi
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on Twitter data and compare it to state-of-the-art methods. Our experiments show significant performance improvements compared to state-of-the-art methods. Table 1: Performance comparison of our approach to state-of-the-art methods for gender inference. |
| Researcher Affiliation | Academia | The provided text snippet does not include explicit author affiliations (university names, company names, or email domains). Therefore, a definitive classification is not possible based solely on the provided text. However, publishing in the 'Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)' strongly suggests an academic context. |
| Pseudocode | No | The paper does not contain structured pseudocode or clearly labeled algorithm blocks. It includes equations and a figure illustration, but no pseudocode. |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We use the following datasets: Twitter, Flickr, and Wikipedia. These are standard academic datasets, commonly publicly available. The paper also provides a bibliography indicating citations for relevant work, implying formal referencing. |
| Dataset Splits | Yes | We split the data into 70% training, 10% validation, and 20% test. |
| Hardware Specification | Yes | All experiments were conducted on a workstation with an Intel Core i7-4770K CPU and 16GB RAM. |
| Software Dependencies | Yes | The experiments were implemented in Python 2.7 using the scikit-learn library version 0.16.1 and CPLEX 12.6. |
| Experiment Setup | Yes | We split the data into 70% training, 10% validation, and 20% test. This provides specific dataset split information as part of the experimental setup. |