How New Models Open Opportunities to Re-Understanding and Expanding Established Insights: Reaction and Critique of Elke Winter’s Us, Them, and Others
Bibliographic record
Abstract
This research note offers critical reaction to Elke Winter’s Us, Them and Others in an effort to trigger debate around the issues she identifies and her model (us + others 1-n = multicultural we ≠ them 1-n ). Critiques include: a looseness and inconsistency with some key concepts and arguments, a narrow sample and timeframe of analysis, and missed opportunities to revisit past luminaries that identify underlying causal mechanisms rather than descriptive labels alone. Despite these criticisms, it is argued that Winter’s model has the potential to strike a chord with a broad range of scholars 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 overlooked and under-appreciated scholarship. Cette note de recherche propose une réaction critique contre Us, Them and Others par Elke Winter, dans le but de stimuler un débat autour des problèmes et du modèle présentés dans ce livre (nous + autres 1-n = un nous multiculturel ≠ eux 1-n ). Les défauts relevés sont, entre autres, une imprécision et des incohérences dans certains concepts clés et arguments principaux, une analyse portant sur un échantillon et une période restreints, et des occasions manquées pour revoir des théories classiques sur les mécanismes de causalité au lieu de simplement s’en tenir à un étiquetage descriptif. Malgré ces critiques, il est clair que le modèle de Winter a beaucoup de potentiel pour trouver un écho auprès d’une grande variété de chercheurs parce qu’il aborde des questions qui sont au cœur des débats contemporains et qu’il offre de nouvelles perspectives nous permettant de revenir sur des travaux négligés et sous-estimés.
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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 | 0.061 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.083 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".