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..
Computational Social Choice: Some Current and New Directions
Authors: Haris Aziz
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is an accompanying paper of my IJCAI 2016 Early Career Spotlight invited talk. The purpose of this short paper is to mention some current and new trends with COMSOC. |
| Researcher Affiliation | Academia | Haris Aziz Data61 and UNSW, Sydney, Australia EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is a survey and review of existing work and does not describe a new methodology for which open-source code would be provided. |
| Open Datasets | No | The paper is a survey and does not present new experimental results based on specific datasets, thus it does not provide access information for a dataset. |
| Dataset Splits | No | The paper is a survey and does not present new experimental results requiring training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is a survey and does not detail any experimental procedures or setups, hence no hardware specifications are provided. |
| Software Dependencies | No | The paper is a survey and does not detail specific experimental procedures, thus it does not list software dependencies with version numbers. |
| Experiment Setup | No | The paper is a survey and discussion of research trends, not an experimental paper, and therefore does not provide details about experimental setup or hyperparameters. |