---
license: cc-by-nc-4.0
language:
- en
- fr
size_categories:
- 100K<n<1M
---
# Refugee Law Lab: Luck of the Draw III: 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.

This is the dataset used for a [research project](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4322881) published in the Queen's Law Journal, undertaken at the Refugee Law Lab about outcomes in stays of removal in Canada's
Federal Court. Specifically, it includes information from the online Federal Court dockets for all immigration law cases filed between
1997 and 2022.

The dataset 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 Instance

The datset includes a single data instance of all online Federal Court dockets involving immigration law filed between 1997 and 2022, 
as they appeared when the data was gathered in November 2022.

### Data Fields

Data fields match the formart used for the Refugee Law Lab's [Canadian Legal Data dataset](https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data).

- citation (string): Legal citation for the document (neutral citation where available). In this dataset, the legal citaiton is the docket number, which is a identifer for the file assigned by the Federal Court. Docket numbers take the form IMM-#-YY. IMM signals that this is an immigration law docket, # is a sequential number starting at 1 that represents the order in which applications were received in a given year, and YY is the last two digits of the year in which the application was initially filed.

- year (int32): Year of the document date, which can be useful for filtering. For this dataset, the year is the year when the application was initially filed.

- name (string): Name of the document, in this dataset the style of cause of a cour file

- date_filed (string): Date of the document (yyyy-mm-dd). In this dataset the year is the date the application was filed.

- city_filed (string): City where the application was initially filed

- nature (string): A category of proceedings assigned by the Federal Court

- class (string): A second category of proceedings assigned by the Federal court

- track (string): A third category of proceedings assigned by the Federal Court

- documents (list of dictionaries): A list of dictionaries containing each docket entry (or row in the table of docket entries in a docket). Each dictionary contains the following key/value pairs:

      * RE_NO: The number assigned to the docket entry by the Federal Court
       
      * DOCNO: Where the entry involves the filing of a document, the number assigned to that document by the Federal Court
       
      * DOC_DT: The date of the docket entry

      * RECORDED_ENTRY: The content of the docket entry

- 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)


### Data Languages

Some dockets are in English, some in French, and some alternate between English and French

### Data Splits

The data has not been split, so all data is in the train split. 

### Data Loading

To load the data:

```python
  from datasets import load_dataset
  dataset = load_dataset("refugee-law-lab/luck-of-the-draw-iii", split="train")
```

To convert to dataframe:

```python
  from datasets import load_dataset
  dataset = load_dataset("refugee-law-lab/luck-of-the-draw-iii", split="train")
```

## Dataset Creation

### Curation Rationale

The dataset includes all Federal Court immigration law dockets available on the Federal Court's website at the time of research (November 2022). The Refugee Law Lab gathered this data for several projects, including the [Refugee Law Lab Portal](https://rllp.ca/) and the research article on Federal Court stays linked above.  

### Source Data

#### Source

All data was gathered via the Federal Court's [website](https://www.fct-cf.gc.ca/en/home).

#### Initial Data Collection and Normalization

Details are available via links on the Refugee Law Lab's Github respository [Luck of the Draw III: Code & Data]
(https://github.com/Refugee-Law-Lab/luck-of-the-draw-iii).

### Personal and Sensitive Information

Documents may include personal and sensitive information. All documents have been published online by the Federal Court. While the open court principle mandates 
that court materials be made 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, 
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 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 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 Federal Court or 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

Due to the ways that all
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](https://www.fct-cf.gc.ca/en/pages/important-notices) for data obtained from the Federal Court, 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, "Luck of the Draw III: Code & Data" (2023) online: Github: <https://github.com/Refugee-Law-Lab/luck-of-the-draw-iii>.

### Acknowledgements

This project draws on research supported by the Social Sciences and Humanities Research Council, the Law Foundation of Ontario, and the Digital Research Alliance of Canada. Jacob Danovich assisted with the infrastructure and scraping code for this project.