Peer Review 1 - Jin Zhang

Summary This paper tries to predict and suggest ways to stop school shootings in the US in 2025. It uses a lot of data and strong analysis to show what could happen and how to prevent it.

Strong Positive Points

Good Use of Data: The paper uses detailed data from The Washington Post to talk about school shootings. This helps us understand the issue better.
Good Analysis and Data Visualization: The paper does a great job analyzing the data with advanced statistics to find out important patterns with clear data visualization.
Well Organized: The paper has clear sections that make it easy to follow the ideas from start to finish.

Critical Improvements Needed

Finish the Paper: Important parts like the abstract, introduction, results, and discussions are missing. These need to be completed.
Submit LLM Documentation: Remember to submit the required LLM documentation to avoid getting 0.
Add More Visuals: More charts, graphs, and tables and their explanation could help make your ideas clearer and more interesting.

Suggestions for Improvement

Better Introduction: Adding more background about the problem and some findings could strengthen the paper. Follow the rubric.
Explain Models Better: Giving more details about why certain models were chosen could help readers understand the choices better.
Provide the solutions: Talking more about how these predictions could change real policies would make the paper more useful.

Evaluation

R/Python cited: 1/1 pts: Proper citation of R/Python libraries used.
Data cited: 1/1 pts: Data sources are properly cited.
Class paper: 1/1 pts: Meets the requirements.
LLM documentation: 0/1 pts: Documentation not submitted.
Title: 2/2 pts: Title is clear and appropriate.
Author, date, and repo: 2/2 pts: Authorship, date, and repository link are correctly provided.
Abstract: 0/4 pts: Abstract section is missing.
Introduction: 0/4 pts: Missing part
Estimand: 1/1 pts: Clear definition of the estimand.
Data: 8/10 pts: Data section is well-detailed but could include more statistical analysis.
Measurement: 4/4 pts: Measurement techniques are well-defined and appropriate.
Model: 5/10 pts: Model section is partially complete; needs more detailed explanation and justification.
Results: 0/10 pts: Results section is not included.
Discussion: 0/10 pts: Discussion section is missing.
Prose: 4/6 pts: Generally well-written but some sections lack clarity.
Cross-references: 1/1 pts: Adequate cross-referencing within the document.
Captions: 2/2 pts: Graphs and tables have appropriate captions.
Graphs/tables/etc: 2/4 pts: Some graphs and tables are included but could be more informative.
Surveys, sampling, and observational data appendix: 8/10 pts: Comprehensive but some minor details are missing.
Referencing: 2/4 pts: Some references are missing or improperly formatted.
Commits: 2/2 pts: Commit history is consistent and informative.
Sketches: 0/2 pts: Sketches section is not completed.
Simulation: 4/4 pts: Simulations are well-executed and relevant.
Tests: 4/4 pts: Comprehensive testing of data.
Parquet: 1/1 pts: Proper use of parquet files for data storage.
Reproducible workflow: 3/4 pts: Workflow is mostly reproducible, but some scripts lack detail.
Enhancements: 2/4 pts: Some innovative enhancements are included, but more could be explored.
Miscellaneous: 2/3 pts: Covers most miscellaneous criteria but lacks some minor elements.
Estimated overall mark: 62/100.

Any other comments: Really good idea! The paper is well on its way to being a great resource on school shootings. Finishing the missing parts and adding more visuals could improve it a lot!

chuyun2024 / School-Shooting-Casualty-Analysis

Peer Review 1 - Jin Zhang #3