Buy American: An Introspective Look into National Corporate Consciousness
Bibliographic record
Abstract
My research is interested in examining product perceptions and the importance of national identity on the marketability on a variety of foreign and domestic consumer products. I am also interested in determining whether the process of globalization has weakened an individual’s sense of national identity and whether that changes their preference for purchasing a foreign and/or domestic product. Primarily, my research question asks whether nationalism influences a product’s marketability. My hypothesis suggests that individual perceptions are heavily influenced by a sense of nationalism and ultimately affects an individual’s decision whether or not to buy a foreign good. To test this hypothesis, I constructed two original surveys that were distributed to university students at two different universities in two separate countries, Wilfred Laurier University in Canada and Georgia Southern University in Georgia, US. From my surveys, I have found that despite the advance of globalization and the integration of markets, it appears that student consumers still tend to identify themselves with products and corporations that they perceived as domestic. When asked, they chose domestic products as a means for reaffirming their national identity. Thesis Mentor: ________________________ Dr. Darin Van Tassell Honors Director: _______________________ Dr. Steven Engel April 2015 Center for International Studies University Honors Program Georgia Southern University
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".