International Students Making Refugee Claims: How Data + Chinook Might Meet

As a recent tweet from Steven Meurrens shows, the relationship between refugee claimants and international students is one that IRCC actively tracks.

Number of Asylum Claims Made By Applicants with Study Permits from 2018 to 2022, Broken Down by Designated Learning Institution

Chart shows schools with most claims in absolute numbers.

Full data set can be found here – https://t.co/42XyeKaSzS pic.twitter.com/L0uOCXs6Gb

— Steven Meurrens (@smeurrens) November 28, 2023

Thanks to an Access to Information and Privacy (“ATIP”) request received earlier this year, we finally have our first look into the question of how this data might be operationalized and related to the use of the Chinook’s application processing system.

The example, we have is from January 2021 in a document titled “By the Books: Analysis of Sri Lankan Student Claimants” from IRCC’s Migration Integrity, Integrity Risk Management Branch. The document is highly redacted so we can only share what we know and speculate about what the document might otherwise also say. I have provide the document for review below

What the Document Says

The first part of the document summarizes four key takeaways, with part of the first and last two being redacted. Based on what we see, the summary captures the change in asylum claimants alongside changes in temporary residence and temporary resident visa issuance. This document coinciding with the pandemic, this also factors in. It is likely that the missing points pertain specifically to study permit holders, given the nature of the document and the redactions.

The Background section then delves into a bit of a country-conditions summary of Sri Lanka coinciding with entry as a top 20 source country for asylum claims in Canada. Those who practice in refugee law, will draw similarities to some of the reports found in National Documeptatin packages here. One of the pinpoints of this particular document is of the Tamil diaspora, tying into the entry of Sri Lankan nationals in 2009-2010 from the much documented Ocean Lady and Sun Sea marine vessels.

Keeping in mind this was nearly three years ago, the amount of data in this document is staggering – from tracking the mode of arrival (air versus other methods), versus the documentation of those with status in Canada. The document provides a graph comparing the number of TR approvals and asylum claims before pinpointing students as having the highest claim rate among TR business lines from Sri Lanka.

The data even tracks the time frame from TR (temporary resident) issuance to Asylum Claim date, showing the Government tracking that a majoirty of students intendes to claim when they acquired their study permit. The data further delves into what level of study the students are in. In a redacted section, this data also goes into what select educational institutions they are coming from, noting over half are at select universities.

Based on an unredacted foot note, there is a reference to “Fraudulently obtained S-1 TRVs for Cape Breton University by CBSA, November 2019.”

This data is then combined with nom-compliance and IRCC’s compliance reporting history, aligning with key indicators of potential non-compliance. While it is redacted, it would be interested to see how they tracked compliance alongside actual claims made, given once an individual makes a claim they acknowledge their inadmissibility and be motivated to discontinue studies. The redactions in this section make it difficult to parse this question of whether non-compliant studies led to claims, or claims led to non-compliant studies.

A fully redacted section called “Additional Observation: Address Clustering” presumably talks about the cities in which these claimants are living. This is another further factor that is likely built into the recommendation below.

The Recommendation of Chinook Module 5 Indicators

The Next Steps section is nearly fully redacted, but the first discussed step is not and is very telling in what it states:

Immediate Actions

1. Creation and Distribution of Sri Lankan Student Indicators

Action: The MIT recommends the construction of indicators for use by visa processing officers in Chinook Module 5. Similar to indicators used in other lines of buisness in other source countries for claims, these indicators will assist officers in identifying potential high risk cases in the Sri Lankan cohort.

Based on the above document, one would suggest that the possible redactions might even pinpoint what those risk indicators look like. I am wondering also how the risk indicators would pick up an individual as Tamil (likely through language), but then also layer on the educational institutional they are attending, the city they are living in (the pockets) and possibly other factors that are discussed in the redacted sections.

Implications: Understanding How Risk Indicators are Created

While we have known for awhile that refugee claims are consideredadverse outcomes for international students, this document truly challenges the breadth and scope of the type of data being used to back the data-based systems within Chinook’s Module 5 Risk Indicators. It suggests that there are data-based calculations and equations being drawn and made of all applicants and form factors outside of an Applicant’s, and currently a reviewing court’s, control.

We have written about risk indicators in the past:

Why If There’s No “N/A” Risk Flag on Your GCMS Notes, You May Have Been Risk Flagged

In this blog, we talk about how these indicators feature prominently in the spreadsheet presentation used by Officers to determine cases.

We’ve also shared that these risk indicators are being flagged through automation and AI on files (see: Integrity Trends Analysis Tool Algorithmic Impact Assessment) and discussed how we have had our own experience litigating a case involving risk flags that were made visible (accidentally) in GCMS, and which may have contributed to a refusal rendered on entirely different grounds than the risk indicators indicated. Through my discussion of bulk decision-making, we discussed how refusals are grouped into buckets that could be informed by the presence of things such as risk indicators.

We know from the IRCC’s response to CIMM Study 8, the following:

• For indicator management within the Chinook tool, risk indicators are used to notify officers oftrends that IRCC has detected or highlight a particular factor of concern, not to sort visaapplications. Keywords are also used to identify positive considerations such as applications thatmay require expedited processing (e.g. conferences, weddings).

• Risk indicators are identified and submitted for entry into Chinook by IRCC officers. Indicatorsand keywords are not created by the Chinook tool.

• The release of specific keywords connected to investigative techniques, trends, and risk profilescould encourage fraud or facilitate the commission of an offence, and are therefore not releasedper section 16(1)(b) of the Access to Information Act.

• Statistics on the use of indicators and keywords are not tracked globally. If indicators or keywordsare present on an individual application, they would appear in notes in GCMS. Where there are noindicators or word flags on a case processed with Chinook, a “N/A” (not applicable) would appearin the relevant field in GCMS.

However, because of the function of the information being redacted, in only rare ‘accidental’ disclosure cases have we seen what these look like and so far, without the detail of the actual words or combinations flagged.

As far as I am aware, this is the first case where we have seen the actual directive or recommendation to create risk indicators as a function of data, and in cases where the risk (or adverse outcome) is seen as a student making a refugee claim.

My big question is about the data. I provided one example above where I challenge an alleged causation between refugee claimants and non-compliant studies. I can think of other issues, such as how IRCC collects data on whether someone is Tamil are not (presumably through language), but from my own personal knowledge of agents playing a large role in applications arising from Sri Lanka, are there possible assumptions being made on forms that are not competently filled out? Are there missing disaggregations?

The follow-up question is data-vetting. If these indicators are not being tracked, but simultaneously there are is a six month review period for these indicators, who (if anyone) is in the room to interrogate this data or allege possible bias or problematic collection?

Given the power of these indicators to essentially take applications out of the assembly line to approval, I would suggest much more transparency, and a robust and publicly explainable data review process, needs to be published by IRCC to alleviate concerns that myself and other colleagues have about this process.