Under the advisement of Professor Bernard SimoninThe Hispanic Paradigm: To acculturate or not to acculturate?
Bibliographic record
Abstract
The aim of this thesis is to investigate the process of acculturation of minorities into American mainstream culture, providing a framework that analyzes the most important factors that influence the process of acculturation. The minority analyzed throughout the research is the Hispanic community because of two main reasons. The first reason is the controversial arguments published recently by Samuel Huntington that alert that Hispanics, especially Mexican-Hispanics, do not acculturate into American culture, which could potentially fragment U.S. culture for the first time in its history. The second reason why I have decided to prove these factors of acculturation in Hispanics is because it is the largest minority in the United States since 2002, with the high probability of becoming the second largest concentration of Hispanics in the world in less than ten years and the eighth largest purchasing economy in the world, ahead of Canada by 2007. Once understood all the factors and differences that affect the acculturation of Hispanics, the ending chapter will be dedicated to understand the marketing implications derived from the emergence of this minority, understanding how to segment most efficiently and analyzing within each segment different consumer behaviors, media consumption and more efficient ways to target and understand this important and growing segment of the U.S. population.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".