Datasets:

refugee-law-lab
/

canadian-legal-data

	---
	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](https://refugeelab.ca) 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

	- SCC: Full text of Supreme Court of Canada decisions, based on the Refugee Law Lab's
	[Supreme Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/scc/) (1877 – present) (updated Apr 1 2024)
	- FCA: Full text of 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](https://refugeelab.ca/bulk-data/fca/) (2001-present) (updated Apr 1 2024)
	- FC: Full text of Federal Court (Canada) decisions that have been given a neutral citation, based on the Refugee Law Lab's [Federal Court Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/fc/) (2001-present) (updated Apr 1 2024)
	- TCC: Full text of Tax Court of Canada decisions that have been given a neutral citation, based on the Refugee Law Lab's [Tax Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/tcc/) (2003-present) (updated Apr 1 2024)

	#### Tribunal Decisions

	- CHRT: Full text of Canadian Human Rights Tribunal decisions, baed on the Refugee Law Lab's [Canadian Human Rights Tribunal Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/chrt/) (2013 – present) (updated Apr 1 2024)

	- RAD: Full text of Immigration and Refugee Board, Refugee Appeal Division decisions, based on the Refugee Law Lab's [Refugee Appeal Division Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/rad/) (2013-present) (updated Apr 1 2024)

	- RPD: Full text of Immigratoin and Refugee Board, Refugee Protection Division decisions, based on the Refugee Law Lab's [Refugee Protection Division Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/rpd/) (2002-2020) (updated Dec 1 2023) Note that this is a legacy dataset as the IRB is no longer publishing RPD decisions. For more recent RPD decisions obtained via Access to Information Requests see the RLLR dataset.

	- RLLR: Full text of Immigration and Refugee Board, Refugee Protection Division decisions, as reported in the [Refugee Law Lab Reporter](https://refugeelab.ca/rllr), based on the Refugee Law Lab's [RLLR Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/rllr/) (2019 – present) (updated Apr 1 2024)

	- SST: Full text of Social Security Tribunal of Canada decisions, baed on the Refugee Law Lab's [Social Security Tribunal Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/sst/) (2013 – present) (updated Apr 1 2024)

	#### Legislation & Regulations

	- LEGISLATION-FED: Text of Federal legislation in the [Consolidated Acts](https://laws-lois.justice.gc.ca/eng/acts/) maintained by the Federal Department of Justice, as reproduced in the Refugee Law Lab's [Federal Bulk Legislation Dataset](https://refugeelab.ca/bulk-data/legislation-fed/) (updated May 13 2024)

	- REGULATIONS-FED: Text of Federal legislation in the [Consolidated Regulations](https://laws-lois.justice.gc.ca/eng/regulations/) maintained by the Federal Department of Justice, as reproduced in the Refugee Law Lab's [Federal Bulk Regulations Dataset](https://refugeelab.ca/bulk-data/regulations-fed/) (updated May 13 2024)


	### 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:

	```python
	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:

	```python
	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](https://refugeelab.ca/bulk-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](https://refugeelab.ca/bulk-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](https://citizenlab.ca/2018/09/bots-at-the-gate-human-rights-analysis-automated-decision-making-in-canadas-immigration-refugee-system/)
	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](https://www.migrationtechmonitor.com/ways-to-help)
	working in this space, and to [listen directly](https://www.migrationtechmonitor.com/about-us) 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](https://creativecommons.org/licenses/by-nc/4.0/))

	NOTE: Users must also comply with upstream licensing for the [SCC](https://www.scc-csc.ca/terms-avis/notice-enonce-eng.aspx),
	[FCA](https://www.fca-caf.gc.ca/en/pages/important-notices) & [FC](https://www.fct-cf.gc.ca/en/pages/important-notices) 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](https://www.osgoode.yorku.ca/faculty-and-staff/rehaag-sean), 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](https://huggingface.co/datasets/pile-of-law/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).