Bibliographic record
Abstract
The development of many nations came about through the act of people migrating from one area to another and thus has been crucial to the formation of countries worldwide. Immigration policies are enacted to regulate who enters the country in efforts to keep economic stability and national security. In various periods throughout time, views and policies on immigration have shifted from more relaxed and accepting of immigrants to firmer and less accepting of immigrants. It is widely acknowledged that during times of economic prosperity and less world conflict, immigration policies tend to be more relaxed, and the opposite holds true in times of greater tension and greater economic struggle. In the past couple decades, we have seen the topic of immigration in the middle of controversial statements and have heard opposing arguments on the effects immigration can have on an economy. States vary on how "strict" the policies are and their overall requirements for admittance. Likewise, it is also important to note that states also vary on their main "pull factors," affecting the number of immigrants who desire to enter said country. For this research, I will be looking at the relationship between a country’s migrant stock, labor force, population growth rate, trade as a percentage of GDP and unemployment in regard to their effects on a country’s gross domestic product (GDP per capita). Specifically, the study looks at developed economies such as the United States, Japan, Canada, Spain, and China during the early 1990's to present in order to study how immigration has impact the countries' labor market and economic development. Overall, this study found that an increase in immigration ultimately leads to an increase in gross domestic product per capita and have little to no effect on unemployment rates.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".