Bibliographic record
Abstract
Over the past few years, there have been several changes in the US and Canadian refugee policies, as well as changes in international refugee law and global sentiments towards refugees.In this thesis, I will be analyzing the Safe Third Country Agreement (STCA) between the US and Canada, which came into force in 2004.I will look at the debates and arguments that surrounded the STCA during its early stages and how those debates have evolved since its implementation.I will be exploring whether these changes are extensive enough to render the STCA ineffective.I will also unpack the meaning of major concepts and terms within the 1951 Refugee Convention and the 1967 Protocol.I hope to determine to what extent these new developments have affected the status of the STCA, whether it should be suspended or eliminated, and what that would mean for security and border management strategies.also to share the burden of refugee and asylum seekers at the border ("Canada-US Safe Third Country Agreement", 2004).When the STCA came into force in 2004, the refugee determination systems of Canada and the US were fairly similar, and their sentiments towards refugees and asylum seekers were also similar.Since its inception, however, Canada and the US have evolved, and their refugee policies have changed.After the election of Donald Trump, the Trump administration has been ending policies and programs which have benefitted refugees and migrants in the past.For example, the Deferred Action for Childhood Arrivals (DACA) allowed people who were brought into the US as undocumented children to legally work and live in the US.Another program, Temporary Protection Status (TPS) gave temporary protection to people who are fleeing not only conflict situations but environmental disasters and economic turmoil.These are only some of the programs ending under the Trump era.Furthermore, since the election, the Trump administration has been clamping down on migration.During the first few months of his term, President Trump established severalExecutive Orders (EOs) that negatively affected refugees, including an order banning refugees from seven Muslim-majority countries, and this EO was informally known as the "Muslim travel ban" (Executive Order No. 13769, 2017).Parallel to this, there has been an increase in anti-migrant and anti-foreigner sentiments in the US, and 2017 saw protests against refugees and asylum seekers, most notoriously in Charlottesville, Virginia.These developments have caused a rise in irregular asylum seekers at the US-Canada border who are crossing into Canada using dangerous routes to circumvent the STCA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".