Position: AI/ML Influencers Have a Place in the Academic Process
Authors: Iain Weissburg, Mehir Arora, Xinyi Wang, Liangming Pan, William Yang Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers, with median citation counts 2-3 times higher than those of the control group. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, USA 2Department of Computer Science, University of California, Santa Barbara, USA. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not state that source code for its methodology is made publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes compiling a dataset from public sources (S2, dblp) but does not provide a link or specific access information for their compiled dataset of over 8,000 papers. It does not refer to a publicly available dataset in the typical sense of a pre-packaged, accessible resource for others to use, nor does it make its own dataset available. |
| Dataset Splits | No | The paper does not involve training a machine learning model, and thus does not describe training, validation, or test dataset splits. It employs comparative analysis using 'target' and 'control' sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the analysis or experiments (e.g., GPU/CPU models, cloud instances). |
| Software Dependencies | No | The paper mentions software like SciPy, SPECTER2, AMiner Scholar Gender Prediction API, and Nominatim geocoding API, but does not provide specific version numbers for any of them. For instance, it mentions 'the implementation available in SciPy' without a version. |
| Experiment Setup | No | The paper conducts data analysis and statistical inference, not machine learning model training. Therefore, it does not provide details like hyperparameters, learning rates, batch sizes, or optimizer settings typically found in experimental setups for model training. |