Multidimensional Polarization, Social Classes, and Societal Conflict: Evidence from Medieval Towns
Bibliographic record
Abstract
The paper focuses on the nature of a population distribution (polarized or not) and its possible influence on societal conflict. Despite theoretical and empirical studies on the link between population’s polarization and social conflict, the relationship remains in question. Up to now, the role of a multidimensional polarization has been neglected and the determination of social classes by their roles and functions (and not by their resource level) has been ignored. To extend the research, we first define a multidimensional polarization index and approach it empirically through quantitative and qualitative data (often textual data) over a very long period in accordance with the historiographical method. First, this paper refutes the stereotype of a medieval French urban population polarized between rich and poor. Second, over the same period, we build a database of the intensity and occurrence of societal conflict on a sample of twenty-four French towns. The paper finds that over time the low initial degree of the population’s polarization continued to decline while societal violence was increasing. Third, whereas polarization is excluded as a determinant of societal conflict, the inter-group heterogeneity measure (or social distance) highlights some relationships. The results show that societal upheavals may be quite connected with the social distance index defined between the high and middle classes; moreover, this social unrest may be greatly related with the index defined between the high and the low classes. By contrast, the results find an outbreak of societal conflicts when social distances between the middle and low classes decrease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".