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..
A Parameterized Perspective on Protecting Elections
Authors: Palash Dey, Neeldhara Misra, Swaprava Nath, Garima Shakya
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct an empirical study to understand how simple defending strategies perform for two such statistical voter generation models. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology, Kharagpur, India 2Indian Institute of Technology, Gandhinagar, India 3Indian Institute of Technology, Kanpur, India |
| Pseudocode | No | The paper describes algorithms (GREEDY 1 and GREEDY 2) in natural language but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We generate 1000 preference profiles over these alternatives for n = 12000, where each vote is picked uniformly at random from the set of all possible strict preference orders over m alternatives. |
| Dataset Splits | No | The paper describes synthetic data generation but does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | Fix m = 5. We generate 1000 preference profiles over these alternatives for n = 12000, where each vote is picked uniformly at random from the set of all possible strict preference orders over m alternatives. The x-axis shows different values of kd and we fix ka = 12 kd. |