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case_11124462 (unknown)
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IN THE UNITED STATES DISTRICT COURT
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FOR THE MIDDLE DISTRICT OF PENNSYLVANIA
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JEREMY SPAK, : No. 24cv185
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Plaintiff :
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: (Judge Munley)
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v. :
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THE OSCAR SMITH COMPANY; and :
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CLIFFORD MUENCH, owner and :
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president of Oscar Smith, :
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Defendants :
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MEMORANDUM
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Plaintiff Jeremy Spak asserts claims against his former employer, the
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Oscar Smith Company (“Oscar Smith’), for disability discrimination and retaliatior
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pursuant to the Americans with Disabilities Act, as amended, 42 U.S.C. §§
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12101, et seg. (‘ADA’) and the Pennsylvania Human Relations Act, 43 PA. STAT.
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§§ 951, et seg. (‘PHRA’). Spak also alleges that Defendant Clifford Muench,
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Oscar Smith’s owner and president, is liable for unlawful and discriminatory
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employment practices under the PHRA’s aiding and abetting provisions.
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Before the court is a motion filed by defendants to dismiss Spak’s
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complaint pursuant to Federal Rule of Civil Procedure 12(b)(6). (Doc. 7).
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Defendants’ motion is ripe for disposition.
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Background
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At the heart of this case are Spak’s allegations that defendants unlawfully
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terminated his employment with Oscar Smith because of his history of drug
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abuse—a decision he contends was driven by discriminatory bias rather than
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legitimate business reasons. Defendants hired Spak as a general laborer in the
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Oscar Smith’s office located in Swoyersville, Pennsylvania in June 2022.‘ (Doc.
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1, Compl. J 15). Spak performed his job well, without any misconduct, for
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approximately six (6) months. (Id. 16). As alleged, during his employment with
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Oscar Smith, Spak received positive feedback. (Id.)
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Plaintiff contends that he voluntarily checked himself into an in-patient
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rehabilitation program for opiate addiction more than six (6) months before
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commencing his employment with Oscar Smith. (Id. | 17). According to Spak’s
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complaint, he was in rehab from the first week of November 2021 until
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Thanksgiving of 2021. (Id. J 18). It is alleged that Spak had not used opiates or
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other illegal drugs since completing rehabilitation. (Id. | 19). Specifically, Spak
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avers that he did not engage in the use of illegal drugs during his employment
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with Oscar Smith. (Id.)
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1 These background facts are derived from plaintiffs complaint. At this stage of the
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proceedings, the court must accept all factual allegations as true. Phillips v. Cnty, of
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Allegheny, 515 F.3d 224, 233 (3d Cir. 2008) (citations omitted}. The court makes no
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determination, however, as to the ultimate veracity of these assertions.
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At the time of his hiring nor at any point during his employment, did Spak
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voluntarily disclose his history of addiction to Oscar Smith or its owner and
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president, Defendant Muench. (Id. {| 20). However, as alleged, he may have
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alluded to his history of addiction during a conversation with Defendant Muench’s
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son, Kevin Muench. (Id. 21). The conversation concerned Kevin Muench's
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sister—also Defendant Muench’s daughter— and focused on her repeated stints
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in rehabilitation programs. (Id,) According to Spak, he may have remarked that
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he could relate to her situation, but did not reveal that he also had undergone
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rehabilitation for substance abuse. (ld. J 22). The complaint does not state when
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the conversation between Kevin Muench and Spak took place.
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On December 12, 2022, Defendant Muench, Oscar Smith’s owner and
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president, instructed Spak through a text message to attend work early in the
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morning. (Id. 9] 23). When Spak arrived to work, Muench was the only one
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present at the shop. (Id. / 24). Muench informed Spak that he would provide the
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transportation to the job site. (Id.) While on the road, Defendant Muench
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allegedly inquired about Spak’s drug recovery and whether he was attending
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“any meetings or doing anything else to stay sober.” (Id. {[ 25). In response,
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Spak explained that he was a “one and done” case as he had voluntarily
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committed himself to rehabilitation when he decided to end his drug use. (Id. □
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26). Spak further explained to Defendant Muench that he did not believe he
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needed to attend sobriety meetings. (Id.) Spak also emphasized that each
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individual is different. (Id.) Per Spak, he thought the conversation went well as
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he continued the rest of the workday without incident. (ld. {J 27-28).
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The following day, however, Defendant Muench instructed Spak that the
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“job was cancelled and therefore there was no work for him.” (Id. {] 29). Then, on
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December 14, 2022, when Spak called Defendant Muench to inquire whether he
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was needed at work, Muench responded in the negative and told him that he
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would call him later. (Id. ] 30). That same day, Muench called Spak to terminate
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his employment. (id. 31). Muench allegedly told Spak over the phone that he
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could not trust him any longer since he was not attending any sobriety meetings.
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📚 LegalMVP Dataset Collection
This repository contains curated U.S. legal datasets collected for building retrieval-augmented generation (RAG) and other machine learning models in the legal domain. The datasets include U.S. Codes (statutes), federal regulations, and other legal texts in multiple formats.
📂 Repository Structure
legalMVP/ │ ├── regulations/ # Raw legal texts │ ├── USCODE-2022-title15.txt │ ├── USCODE-2023-title15.txt │ ├── USCODE-2023-title26.txt │ ├── USCODE-2023-title26.pdf │ └── ... (other titles/years) │ ├── scripts/ # Data processing & download scripts │ └── fetch_regulations.py # Example: fetches 200 statutes in txt/pdf │ └── README.md # Project documentation
📑 Datasets Obtained
We currently have U.S. Code (statutory law) datasets for multiple years, stored as both .txt and .pdf:
Title 15: Commerce and Trade
USCODE-2022-title15.txtUSCODE-2023-title15.txt
Title 26: Internal Revenue Code (Tax Law)
USCODE-2023-title26.txtUSCODE-2023-title26.pdf
More titles can be added as needed.
🛠️ Data Formats
- TXT → machine-friendly plain text (ideal for preprocessing, tokenization, embeddings, and training).
- PDF → reference copies (useful for citation, legal formatting, and validation).
🎯 Intended Use
These datasets are intended for legal NLP research, specifically:
Retrieval-Augmented Generation (RAG):
Building retrieval pipelines to fetch relevant sections of statutes and regulations before passing them into LLMs.Fine-Tuning / Domain Adaptation:
Adapting open-source LLMs to understand statutory and regulatory language.Information Extraction:
Parsing structured knowledge from unstructured statutes.
⚡ Training Expectations
Input Size:
Legal statutes are long and verbose → chunking (e.g., 512–2048 tokens) is necessary before embeddings.Embedding Models:
Use sentence-transformers or OpenAI embedding models to index statutes for retrieval.RAG Pipelines:
Expect performance gains in precision of retrieval (correctly pulling the relevant statute sections).Evaluation Metrics:
- Retrieval: Recall@k, MRR (Mean Reciprocal Rank).
- QA: Accuracy, BLEU/ROUGE for generated answers.
🚧 Next Steps
Expand Coverage
- Add more U.S. Code titles (e.g., Titles 7, 18, 42).
- Include Code of Federal Regulations (CFR) for regulatory data.
Preprocessing
- Normalize whitespace, remove headers/footers.
- Add metadata (Title, Section, Year).
Embedding + Indexing
- Build vector stores (e.g., FAISS, Weaviate, Chroma).
Model Training
- Train/evaluate RAG pipeline with legal queries.
- Fine-tune LLMs on statute-specific Q&A pairs.
📜 License
- The U.S. Code and federal regulations are in the public domain.
- Scripts and preprocessing logic are released under the MIT License.
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