Let's talk about class: towards an institutionalist typology of class relations in the cities of pre-modern Europe (<i>c</i>. 1200 –<i>c</i>. 1800)
Bibliographic record
Abstract
ABSTRACT For a quarter century, the term ‘class’ has been anathema for most writers of premodern urban history. The term's associations with discredited forms of analysis – forms often dubiously but persistently associated with Marxism – continue to hamper its reintroduction. In the absence of ‘class’, or a term like it, however, meaningful discussion of ‘horizontal’ divisions in urban society has dwindled. The present article suggests that ‘class’ can and should be reintroduced into our analysis, but that this should be done in an informed way, which takes into account the principal possible meanings of the term. To this end, we analyse the ways in which urban historians have employed the term ‘class’ and find four principal usages. Two of these are ‘material’ and two are ‘institutional’. It is further suggested that certain institutions, such as the nobility and town governments in Europe, can be ‘class determining’, insofar as they channel economic and productive differences into effective political, legal and ideological ‘classes’. This insight, and the typology it is based upon, open the possibility for integrating ‘class’ analysis with recent work in both European and Global contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".