Bibliographic record
Abstract
Abstract Responding to Martin, Turner, and Hitlin, we clarify possible misunderstandings of our two papers on “agency.” First, they do not presume or commit us to any form of universal determinism. We merely assume that the job of sociologists is to try and causally explain as much as we can of the variations in social life. Though our best efforts leave huge amounts of variance unexplained, there is no good reason for calling this unexplained variance “agency,” and there are several good reasons for not doing so. Second, we acknowledge our use of “structure” is quite a loose one, simply referring to the combination of environmental and personal factors that can help us explain social phenomena. Our notion of “causation” is, admittedly, no less “slipshod” than that used by most social scientists. We are happy to leave questions as to the true nature of causation to the philosophers. Third, we do not see in what way using the notion of “agency” to describe, much less account for, novelty (Martin), or to help “organize” the potentially infinite number of forces in play (Hitlin), advances our understanding or explanatory power. The normative and voluntaristic connotations of the term only serve to muddy the explanatory waters. Fourth, this doesn't preclude empirically examining the sense of “agency” and its causes and consequences. Even if the current wave of enthusiasm for “agency” is waning, a thorough conversation remains worthwhile if only to help avoid the same confusions popping up again in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".