Repertoires of Comparison: How Common Comparisons Shape Social and Political Life
Bibliographic record
Abstract
Comparison is ubiquitous in social life, yet some comparisons are far more common, institutionalized, and influential than others. Building on work in cultural sociology, we call these repertoires of comparison (RoCs). Institutionalized across one or more social fields, RoCs are employed at different levels of society (local, national, global) and across myriad social contexts (sports clubs, city offices, the media). For example, horrific events are frequently compared to the paradigmatic case of 9/11 to assess their gravity, while Greenwich Village long served as a model neighborhood for municipal policymakers. Our aim in this article is to put RoCs on the sociological map. Although such common comparisons have been indirectly analyzed in scholarship on repertoires, symbolic boundaries, orders of worth, and commensuration, we argue that conceptualizing them as a distinct phenomenon is analytically useful. RoCs matter because they: (a) encode and express moral judgments; (b) shape our frames of reference; (c) guide aspirations and aversions; and (d) inform symbolic boundary drawing. We then discuss how RoCs emerge, become contested, and which social factors contribute to their transformation or replacement. We show how RoCs are a fundamental dimension of social and political life and illustrate how this concept can be used to analyze understudied aspects of research on culture and politics.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.071 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".