The Millennial Influence on Border Crossings - Contemplating the Future of Canada's Economic Security
Bibliographic record
Abstract
The Cross Borders Institute at the University of Windsor has established through its research a crucial foundation for understanding the nature of borders and border crossings in the context of economic stability. Canada’s economic stability and growth with respect to security and trade is proven to be a result of political harmony between the U.S.A and Canada and can be strengthened by borders and border crossing processes that are efficient and equitable. My research encompasses a futuristic approach in examining the connection between this foundational research on borders and border crossings in light of developing trends occurring as a result of the influence of the millennial generation, with specific reference to the increasing use of technology and the ways it is being employed in tandem with emerging generational trends. My research approach is cross disciplinary and examines published literature and research on Canada’s economic security and growth in accordance with borders and border crossings, with a special focus on what Dr. Anderson of the Cross Borders Institute terms the “Binational Advantage”, a concept that supports the flexibility of borders for greater economic security. I cross examine this research with quantitative and qualitative research on millennial trends and how they have affected a variety of societal structures to date most notably a number of markets through consumerism trends and increased reliance on technology. Currently this research is in progress, but my findings so far have shown millennial trends as having an incredible affect on markets by creating and demanding consumerism that is more accessible to the public. This is often done through the means of technology that increasingly permeates every part of our daily lives. There are strong connections between this increase in accessibility demands created by the influence of millennials and the “Binational Advantage” approach advocating for flexibility in border and border crossings for the greater economic good of Canada. Understanding the impact of millennial generational trends on borders and border crossings is important to our understanding of how it may affect Canada’s economic stability and growth as it is intertwined with, and to a degree contingent upon, the very nature of borders and border crossings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".