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Record W1973629911 · doi:10.1080/02722010509481372

Should Canadian Immigration Policy be Synchronized With U.S. Immigration Policy? Lessons Learned at the Start of Two Centuries

2005· article· en· W1973629911 on OpenAlexaboutno aff
Tamara Woroby

Bibliographic record

VenueThe American Review of Canadian Studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationImmigration policyPolitical sciencePublic administrationLaw

Abstract

fetched live from OpenAlex

The turn of both this century and the twentieth century have been accompanied by high levels of immigration into Canada and the United States. Around 1900, as the last century began, there occurred a sharp rise in the number of people arriving from abroad and these high levels did not diminish until the 1930s. Similarly, today, immigration to both Canada and the U.S. has increased to record highs, along with the impression that immigration may somehow be out-of-control. (1) Historically, Canada and the U.S. have implemented independent immigration policies, each establishing its own set of preferred immigrant characteristics and entrance procedures. More recently, after the introduction of the NAFTA, and most certainly since 9/11, a question that has been posed with increasing frequency is: should Canada synchronize its immigration policy with that of the U.S., and if so, to what extent? With the above question in mind, this paper compares and contrasts economic aspects of the Canadian and U.S. immigration experiences, both at the present time and at the turn of the twentieth century. We include the historical perspective so as to see what lessons--relevant for today--might be learned by comparing the present situation with a past high-immigration period. We add to the existing literature by incorporating income inequality data to estimate Canadian inequality for the period at the beginning of the twentieth century. Defining Immigration It is useful to establish what is meant by the word immigrant, because it has become a common error in public discussion to apply this term to describe anyone who crosses a border. In actuality, people move from one country to another for a variety of reasons, and it is on the basis of these reasons and the permanence of their stay, that they are classified into one of two groups: immigrants and non-immigrants. The people who become the immigrants of a country move to take up permanent residence in the new country. This may be for the purposes of a new job, to reunify with family already permanently residing there, or to seek asylum or safe haven as refugees. Immigrants may enter legally or illegally. Over the past two decades, illegal immigration has emerged as an important factor in the U.S., but does not appear to be significant in Canada. Based on an average of the past five years, the annual inflow of legal immigrants to the U.S. has been about 900,000, and the annual inflow of illegal immigrants has been about 350,000 (official estimates). (2) In comparison, the average annual inflow of immigrants to Canada (almost all of them legal) has been about 220,000. (3) The second group of people who move consists of non-immigrants, those who do not intend to become permanent residents and will instead reside in the new country for a temporary period of time. This period may be longer or shorter, depending upon the motive. This group consists of foreign students, temporary workers, business visitors, and tourists. It is this group that dominates when looking at the number of people who cross a border. For example, in a recent year, approximately 1.4 million immigrants (including legal and illegal) and 33 million non-immigrants entered the U.S. For Canada, these numbers were 250,000 for immigrants and 6.2 million for non-immigrants. (4) When we examine a country's immigration policy, we therefore limit ourselves to a very small portion of the actual number of people who cross a border. Focusing on border issues and immigration issues is not the same thing. This is an especially important point to note in light of the fact that the U.S. has become, and will continue to be (into the foreseeable future), preoccupied with border security, which requires examining factors far beyond immigration per se. In light of the tremendous imbalance between immigrant and non-immigrant flows, coordinating immigration policies between Canada and the U. …

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0090.005
Scholarly communication0.0090.005
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.059
GPT teacher head0.393
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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Same venueThe American Review of Canadian StudiesSame topicMigration and Labor DynamicsFrench-language works237,207