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metadata
license: cc-by-nc-4.0
language:
  - en
  - fr
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: '*/*.parquet'
  - config_name: SCC
    data_files:
      - split: train
        path: SCC/*.parquet
  - config_name: FCA
    data_files:
      - split: train
        path: FCA/*.parquet
  - config_name: FC
    data_files:
      - split: train
        path: FC/*.parquet
  - config_name: TCC
    data_files:
      - split: train
        path: TCC/*.parquet
  - config_name: CHRT
    data_files:
      - split: train
        path: CHRT/*.parquet
  - config_name: RAD
    data_files:
      - split: train
        path: RAD/*.parquet
  - config_name: RPD
    data_files:
      - split: train
        path: RPD/*.parquet
  - config_name: RLLR
    data_files:
      - split: train
        path: RLLR/*.parquet
  - config_name: SST
    data_files:
      - split: train
        path: SST/*.parquet
  - config_name: LEGISLATION-FED
    data_files:
      - split: train
        path: LEGISLATION-FED/*.parquet
  - config_name: REGULATIONS-FED
    data_files:
      - split: train
        path: REGULATIONS-FED/*.parquet

Refugee Law Lab: Canadian Legal Data

Dataset Summary

The Refugee Law Lab supports bulk open-access to Canadian 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 text of legislation, regulations, and thousands of court and tribunal decisions at the federal level. It can be used for legal analytics (i.e. 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 for various tasks.

Dataset Structure

Data Instances

Court Decisions

Tribunal Decisions

Legislation & Regulations

Data Fields

  • citation (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)

  • dataset (string): Name of the data instance (e.g. "SCC", "FCA", "FC", "TCC", 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 cases and the short title for legislation and regulations

  • language (string): Language of the document, "en" for English, "fr" for French, "" for no language specified

  • document_date (string): Date of the document (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 Languages

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

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 (where available) are put into the same split.

Data Loading

To load all data instances:

  from datasets import load_dataset
  dataset = load_dataset("refugee-law-lab/canadian-legal-data", split="train")

To load only a specific data instance, for example only the SCC data instance:

  from datasets import load_dataset
  dataset = load_dataset("refugee-law-lab/canadian-legal-data", "SCC", split="train")

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 law and regulations, federal courts (e.g. Supreme Court of Canada, Federal Court of Appeal, Federal Court) and 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 the federal level.

Source Data

Initial Data Collection and Normalization

Details (including links to github repos with code) are available via links on 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 government body, court or tribunal. While the open court principle mandates that court (and some tribunal) materials be made available to the public, there are privacy risks when some of these materials become easily and widely available. These privacy risks are particularly acute for marginalized groups, including refugees and other non-citizens whose personal and sensitive information is included in some of the documents in this dataset. For example, imagine a repressive government working with private data aggregators to collect information that is used to target families of political opponents who have sought asylum abroad. One mechanism used to try to achieve a balance between the open court principle and privacy is that in publishing court 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 unofficial copies. For official versions published by the Government 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 endorsement of the Government of Canada.

Considerations for Using the Data

Social Impact of Dataset

The Refugee Law Lab recognizes that this dataset -- and further research using the dataset -- raises challenging questions about how to balance protecting privacy, enhancing government transparency, addressing information asymmetries, and building technologies that leverage data to advance the rights and interests of refugees and other displaced people, as well as assisting those working with them (rather than technologies that enhance the power of states to control the movement of people across borders).

More broadly, the Refugee Law Lab also recognizes that considerations around privacy and data protection are complex and evolving. When working on migration, refugee law, data, technology and surveillance, we strive to foreground intersectional understandings of the systemic harms perpetuated against groups historically made marginalized. We encourage other users to do the same.

We also encourage users to try to avoid participating in building technologies that harm refugees and other marginalized groups, as well as to connect with community organizations working in this space, and to listen directly and learn from people who are affected by new technologies.

We will review the use these datasets periodically to examine whether continuing to publicly release these datasets achieves the Refugee Law Lab's goals of advancing the rights and interests of refugees and other marginalized groups without creating disproportionate risks and harms, including risks related to privacy and human rights.

Discussion of Biases

The dataset reflects many biases present in legal decision-making, including biases based on race, immigration status, gender, sexual orientation, religion, disability, socio-economic class, and other intersecting categories of discrimination.

Other Known Limitations

Legislation and regulations do not include tables, annexes or schedules. Original sources should be consulted if these are relevant.

Publicly available court and tribunal decisions are not a representative sample of legal decision-making -- and in some cases may reflect significantly skewed samples. To give one example, the vast majority of Federal Court judicial reviews of refugee determinations involve negative first instance decisions even thought most first instance decisions are positive (this occurs because the government seldom applies for judicial reviews of positive first instance decisions whereas claimants frequently apply for judicial review of negative decisions). As such, generative models built partly on this dataset risk amplifying negative refugee decision-making (rather than more common positive refugee decision-making).

Due to the ways that legal datasets may be skewed, users of this dataset are encouraged to collaborate with or consult domain experts.

Additional Information

Licensing Information

Attribution-NonCommercial 4.0 International (CC BY-NC 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 to protect privacy. As a result, users must not make the data available in 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 data. 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: Hugging Face: https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data (updated 2024).

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).