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..
Condorcet Relaxation In Spatial Voting
Authors: Arnold Filtser, Omrit Filtser5407-5414
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that 0.557 ≤ β (Rd, 2) for any dimension d (notice that 1/d < 0.557 for any d ≥ 4). In addition, we prove that for every metric space (X, d) it holds that √2 − 1 ≤ β (X,d), and show that there exists a metric space for which β (X,d) = 1/2. |
| Researcher Affiliation | Academia | 1 Columbia University 2 Stony Brook University |
| Pseudocode | No | The paper contains mathematical proofs and claims but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing source code or provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe any validation dataset splits or processes. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |