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..
The State of the AIIDE Conference in 2017
Authors: Nathan Sturtevant, Brian Magerko
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a descriptive overview of the AIIDE conference's state and trends, rather than an experimental study with data analysis or a theoretical paper presenting new algorithms or proofs. |
| Researcher Affiliation | Academia | Nathan R. Sturtevant Computer Science Department University of Denver EMAIL; Brian Magerko School of Literature, Communication and Culture Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a descriptive overview and does not present any software or code for release. |
| Open Datasets | No | The paper is a descriptive overview and does not use datasets for training or present information about public datasets. |
| Dataset Splits | No | The paper is a descriptive overview and does not involve data splits for validation or training. |
| Hardware Specification | No | The paper is a descriptive overview and does not mention any hardware used for experiments. |
| Software Dependencies | No | The paper is a descriptive overview and does not mention any software dependencies for experiments. |
| Experiment Setup | No | The paper is a descriptive overview and does not describe any experimental setup or hyperparameters. |