BOOK REVIEW How New Models Can Rejuvenate Established Insights: Reaction to and Critique of Elke Winter’s Us, Them, and Others
Bibliographic record
Abstract
Every few years, a book comes along that strikes a chord with readers because it tackles issues that are front and center in contemporary debates and it manages to offer new insights that allow scholars to return to long standing debates.1 Elke Winter’s Us, them and others has the potential to be such a book. It offers a model that draws on literatures of nationalism and those of race and ethnicity using a Weberian approach. It also looks at how different groups align with one another in some contexts, but exclude one another in others and as a result, the book offers insight on multiculturalism, the growing fear of ethnic and religious radicalization, and failed immigration. In this short paper, I offer reaction to, and criticism of, Winter’s model in an effort to trigger broader debate around the issues she identifies and the model she uses to understand them. Despite the many strengths of the book (see Ramos 2011), Winter’s argument is open to a number of criticisms including an openness and inconsistency of key concepts and arguments, a narrow sample and timeframe of analysis, and missed opportunities for revisiting past luminaries in the area that identify underlying causal mechanisms rather than descriptive labels alone. Looseness of Terms and Consistency of the Argument The center piece of the book is Winter’s model (us + others1−n=multicultural we≠ them1−n) which complicates binary distinctions among ethnic and national groups by looking at how they change in different situations. She, however, is inconsistent in her use of “us, ” “Others ” (capitalized), “others, ” and “them. ” “Us ” is almost continuously reserved for English-Canada, but “others”, in both usages, and “them ” are Int. Migration & Integration
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".