Between Description and Evaluation: How Sociologists Do Normativity
Bibliographic record
Abstract
Abstract We argue that the philosophical distinction between the “good” and the “right” is helpful in discerning the plurality of normative stakes in sociological accounts. Our argument stands in contrast to other approaches to the question of normativity in sociology on several grounds. Primarily, we locate the normative content of sociological accounts in their actual explanatory, descriptive, or interpretative empirical models, rather than in their deep theoretical cores. We contend that sociologists inevitably engage in normativity, even when employing empirical constructs that appear unanchored from robust theoretical commitments. Furthermore, we propose that appeals to the good life and justice cut across different types of sociological accounts. Surveying a number of celebrated theoretical and empirical studies, drawn from a variety of sociological subfields, we bring to light how these normative modalities take shape in routine applications and discussions of three sociological concepts: (1) agency, (2) structures, and (3) processes.
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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 | 0.109 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.106 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".