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Refugee Law Lab: Canadian Legal Data

Dataset Description

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Dataset Summary

The Refugee Law Lab supports bulk open-access to Canada legal data to facilitate research and advocacy. Bulk open-access helps avoid asymmetrical access-to-justice and amplification of marginalization that results when commercial actors leverage proprietary legal datasets for profit -- a particular concern in the border control setting.

The Canadian Legal Data dataset includes the unofficial full text of thousdands of court and tribunal decisions at the federal level. It can be used for legal analytics (e.g. identifying patterns in legal decision-making), to test ML and NLP tools on a bilingual dataset of Canadian legal materials, and to pretrain language models.

Languages

Many documents are available in both English and French. Some are only available in one of the two languages.

Dataset Structure

Data Instances

  • SCC: Full text of all Supreme Court of Canada decisions, based on the Refugee Law Lab's Supreme Court of Canada Bulk Decisions Dataset (1877 – 2023)
  • FCA: Full text of all Federal Court of Appeal (Canada) decisions that have been given a neutral citation, based on the Refugee Law Lab's Federal Court of Appeal Bulk Decisions Dataset (2001-2023)
  • FC: Full text of all Federal Court (Canada) decisions that have been given a neutral citation, based on the Refugee Law Lab's Federal Court Bulk Decisions Dataset (2001-2023)

Data Fields

  • citation1 (string): Legal citation for the document (neutral citation where available)

  • citation2 (string): For some documents multiple citations are available (e.g. for some periods the Supreme Court of Canada provided both official reported citation and neutral citation)

  • data_instance (string): Name of the data instance (e.g. "SCC", "FCA", "FC", etc)

  • year (int32): Year of the document date, which can be useful for filtering

  • name (string): Name of the document, typically the style of cause of a case

  • language (string): Language of the document, "en" for English, "fr" for French

  • document_date (string): Date of the document, typically the date of a decision (yyyy-mm-dd)

  • source_url (string): URL where the document was scraped and where the official version can be found

  • scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd)

  • unofficial_text (string): Full text of the document (unofficial version, for official version see source_url)

  • other (string): Field for additional metadata in JSON format, currently a blank string for most datasets

Data Splits

The data has not been split, so all files are in the train split. If splitting for training/validation, some thought should be given to whether it is necessary to limit to one language or to ensure that both English and French versions of the same documents are put in the same split.

Dataset Creation

Curation Rationale

The dataset includes all the Bulk Legal Data made publicly available by the Refugee Law Lab. The Lab has focused on federal courts (e.g. Supreme Court of Canada, Federal Court of Appeal, Federal Court) as well as federal administrative tribunals (e.g. Immigration and Refugee Board) because immigration and refugee law, which is the main area of interest of the Lab, operates mostly at a federal level.

Source Data

Initial Data Collection and Normalization

Details (including links to github repos with code) are available via links for the Refugee Law Lab's Bulk Legal Data page.

Personal and Sensitive Information

Documents may include personal and sensitive information. All documents have been published online or otherwise released publicly by the relevant court or tribunal. While the open court prinple mandates that court (and some tribunal) materials be available to the public, there are privacy risks when these materials become easily and widely available. These privacy risks are particularly acute for marginalized groups, include refugees and other non-citizens whose personal and sensitive information is included in some of the documents in this dataset. One mechanism used to try to achieve a balance between the open court principle and privacy is that in publishing the documents in this dataset, the relevant courts and tribunals prohibit search engines from indexing the documents. Users of this data are required to do the same.

Non-Official Versions

Documents included in this dataset are not the offical versions. For official versions published by the Goverment of Canada, please see the source URLs.

Non-Affiliation / Endorsement

The reproduction of documents in this dataset was not done in affiliation with, or with the endoresment of the government of Canada.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Licensing Information

Creative Commons Attribution 4.0 International (CC BY 4.0)

NOTE: Users must also comply with upstream licensing for the SCC, FCA & FC data instances, as well as requests on source urls not to allow indexing of the documents by search engines (so do not make the data available in other formats or locations that can be indexed by search engines).

Warranties / Representations

We make no warranties or representations that the data included in this dataset is complete or accurate. Data were obtained through academic research projects, including projects that use automated processes. While we try to make the data as accurate as possible, our methodologies may result in inaccurate or outdated datas. As such, data should be viewed as preliminary information aimed to prompt further research and discussion, rather than as definitive information.

Dataset Curators

Sean Rehaag, Osgoode Hall Law School Professor & Director of the Refugee Law Lab

Citation Information

Sean Rehaag, "Refugee Law Lab: Canadian Legal Data" (2023) online: HuggingFace: https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data.

Acknowledgements

This project draws on research supported by the Social Sciences and Humanities Research Council and the Law Foundation of Ontario.

The project was inspired in part by the excellent prior work by pile-of-law (Peter Henderson et al, "Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset" (2022), online: arXiv: https://arxiv.org/abs/2207.00220)