Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T22:43:08.195471Z
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2508.06484.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T22:43:08.195471Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T13:13:31.583222Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T13:14:40.459885Z
33 of 33 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 71e457e7-ab3a-40ee-ae1f-c5cdff0b9b0b · outbound
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data CrowdGenUI: Aligning LLM-Based UI Generation with Crowdsourced User Preferences
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation
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Reference 3
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data InsightPilot: An LLM-empowered automated data exploration system,
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Data Interpreter: An LLM Agent For Data Science
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Alchemist: Llm-aided end-user development of robot applications,
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Cocobo: Exploring large language models as the engine for end-user robot programming,
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data What-if analysis for business users: Current practices and future opportunities,
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Benchmarking data science agents,
Reference 10
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
Reference 11
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models,
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Github copilot ai pair programmer: Asset or liability?
Reference 13
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Observation afbd5ed8-327b-4b06-9dd9-5db49bb0dfa2 · outbound
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Non-expert programmers in the generative ai future,
Reference 14
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Pwr: Exploring the role of representations in conversational programming,
Reference 15
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Intelliexplain: Enhancing conversational code generation for non-professional programmers,
Reference 16
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data COLDECO: An End User Spreadsheet Inspection Tool for AI-Generated Code ,
Reference 17
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Does the whole exceed its parts? the effect of ai explanations on complementary team performance,
Reference 18
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data To rely or not to rely? evaluating interventions for appropriate reliance on large language models,
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Observation 589cb01e-0a30-43ca-8c3a-2975fef8b5dc · outbound
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data IntelliExplain: Enhancing Conversational Code Generation for Non-Professional Programmers
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Intercode: Standardizing and benchmarking interactive coding with execution feedback,
Reference 21
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data LEVER: Learning to Verify Language-to-Code Generation with Execution
Reference 22
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Marketing mix model: In- sights and optimization,
Reference 23
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data The state of the art in end-user software engineering,
Reference 24
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data On Mitigating Code LLM Hallucinations with API Documentation
Reference 25
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Prolific,
Reference 26
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Observation 9d39bd8e-c259-4781-9bd3-16060db1ffe9 · outbound
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Statistical methods for research workers,
Reference 27
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Observation ae6aeb46-2276-405c-bc27-c618febba2a1 · outbound
Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Young people’s descriptions of computational rules in role-playing games: An empirical study,
Reference 31
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Available: https://doi.org/10.1145/1922649.1922658
Reference 2011
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Available: https://doi.org/10.1145/3411764.3445717
Reference 2021
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Available: https://doi.org/10.1145/3491101.3519665
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data PwR: Exploring the Role of Representations in Conversational Programming
Reference 2023
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Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data Available: https://doi.org/10.1145/3663384.3663393
Reference 2024
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From Prompting to Verification: How Experience Shapes Vibe Coding Practices Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Reference 72
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