Classic, Segmented-, or Neo-Assimilation, Which Theory to Use? A Scientific-Method Investigation
Bibliographic record
Abstract
Classic, segmented, and neo-assimilation theories delineate immigrants’ assimilation trajectories. Classic assimilation, since the early twentieth century, treats newcomers’ and established groups’ interactions as leading to ethnoracial pluralism within one culture. Segmented-assimilation, since the 1990s, examines non-European immigrants’ experiences, and considers how factors such as ethnic capital, immigration policies, and racial discrimination cause assimilation into upper, middle, and underclasses. Neo-assimilation, since the early 2000s, posits that assimilation compels upward mobility into a diverse mainstream. While the progenitors of each theory have pointed out the other's deficiencies, in this paper we simultaneously compare the three theories in light of the scientific method's criteria of deductive-inductive hypothesizing and falsification. We find that classic theory follows the scientific method and is falsifiable. In comparison, we find that because segmented- and neo-assimilation each depart from the scientific method in three ways they are not readily testable or falsifiable. We discuss the implications for migration and assimilation research.
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 | 0.165 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".