Samuel Huntington's clash of civilizations hypothesis: challenges from Amartya Sen and the Western South Asian post-immigrant experience
Bibliographic record
Abstract
Samuel Huntington ( 1993, 1996) contends that civilizational identities in our modem world are fundamentally based on major divisions of cultural identity.According to Huntington, a resurgence in cultural identities is reinforcing cultural differences leading to a world characterized by culturally-based civilizational divisions, and a "clash of civilizations".As a counterpoint, Amartya Sen (2006), a Nobel Laureate in Economics, also recognizes the pervasive nature of cultural divisions that are instigating violence and clashes but analyzes and advocates remedies to future, culturally-based clashes.In this project, I explore Huntington and Sen's ideas related to cultural transformation as they pertain to the clash of civilizations hypothesis.Their ideas are compared to three case studies derived from the existing social science literature related to Western South Asian post-immigrant experiences.I offer answers to the following questions: What are Samuel Huntington's and Amartya Sen's conceptualizations of cultural transformation?How well do these conceptualizations apply to the experience of post-immigrants?And does their ability to explain (or not explain) the post-immigrant experience reinforce or weaken the case for a clash of civilizations?In the end, I found that Sen's work best characterizes and explains the post-immigrant experience, and that this calls into doubt the validity of the clash of civilizations hypothesis.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".