What Do We Use “Agency” for? A Critical Empirical Examination of Its Uses in the Sociological Literature
Bibliographic record
Abstract
Abstract The use of the concept of “agency,” in the sense of action that is to some extent free of “structural” constraints, has enjoyed enormous and growing popularity in the sociological literature over the past several decades. In a previous paper, we examined the range of theoretical rationales offered by sociologists for the inclusion of the notion of “agency” in sociological explanations. Having found these rationales seriously wanting, in this paper we attempt to determine empirically what role “agency” actually plays in the recent sociological literature. We examine a random sample of 147 articles in sociology journals that use the concept of “agency” with the aim of identifying the ways in which the term is used and what function the concept serves in the sociological explanations offered. We identify four principal (often overlapping) uses of “agency”: (1) purely descriptive; (2) as a synonym for “power”; (3) as a way to identify resistance to “structural” pressures; and (4) as a way to describe intelligible human actions. We find that in none of these cases the notion of “agency” adds anything of analytical or explanatory value. These different uses have one thing in common, however: they all tend to use the term “agency” in a strongly normative sense to mark the actions the authors approve of. We conclude that “agency” seems to serve the purpose of registering the authors' moral or political preferences under the guise of a seemingly analytical concept.
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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 | 0.101 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.014 | 0.073 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| 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".