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..
Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective
Authors: Huang Xu, Zhiwen Yu, Bin Guo, Mingfei Teng, Hui Xiong
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method. |
| Researcher Affiliation | Academia | Huang Xu1, Zhiwen Yu1, Bin Guo1, Mingfei Teng2, Hui Xiong2 1 Northwestern Polytechnical University, Xi an, Shaanxi, 710129 China 2 Rutgers University |
| Pseudocode | Yes | Algorithm 1: Stochastic job title hierarchy extraction |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We have collected 20,243,120 digital resumes from a large OPN until October 2017. (The paper describes collecting a dataset but does not provide specific access information like a link, DOI, or a citation to a publicly available version.) |
| Dataset Splits | No | Hyperparameters are tuned on 5% of the dataset. (While it mentions using 5% of the dataset for hyperparameter tuning, it does not explicitly provide the full dataset split information (e.g., specific percentages for training, validation, and test sets) to reproduce the experiment’s data partitioning.) |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions that “Hyperparameters are tuned on 5% of the dataset” but does not provide specific values for hyperparameters or other detailed training configurations in the main text. |