Bibliographic record
Abstract
This paper draws on current practice in the qualitative study of ideas to elaborate a set of inferential strategies, grounded in the logic of process tracing, through which scholars can empirically evaluate ideational theories. As I will argue, ideational effects in politics have characteristics that make them difficult to study. The paper nonetheless seeks to demonstrate that ideas and their effects are empirically tractable: in particular, that process-tracing offers a powerful logic for testing well-constructed theories of ideational causation. More specifically, the paper seeks to demonstrate that process tracing of ideational effects can benefit from an expansive empirical scope. It is often tempting for the analyst to zero in on key moments of political decision, on the handful of elite actors who were “at the table,” and on the reasons that they provided (publicly or privately) for their choices. For reasons that I will outline, such a tight focus on critical choice points will rarely be empirically sufficient. To detect ideational effects and distinguish them from alternative possible causes, our analytic field of view must be expand beyond deliberation and argumentation at critical decision points to encompass broader intellectual, sociological, and institutional processes unfolding over considerable periods of time. A well-specified theory of ideas will imply a series of predictions about the observable footprints that ideational mechanisms should leave on a political terrain at multiple points in time and levels of aggregation: not only on individual elites’ statements but also on sequences of events, on flows of information, on organizational membership, on institutional routines, and on the outcomes being explained. Taken together, I will argue, strategies of textual, temporal, organizational, institutional, and outcome analysis can help analysts persuasively distinguish ideational accounts from the materialist or rationalist alternatives. In outlining, illustrating, and assessing these strategies, the paper emphasizes the importance of careful and explicit reasoning with causal-process evidence about ideas. As in all inferential endeavors, analysts seeking to trace ideational processes must relentlessly confront their interpretations of the data with plausible alternatives. In particular, they must justify their inferences by reference to knowledge of and reasoning about the broader context within which decisions unfold. Contextual knowledge is particularly important for the testing of rival, rationalist explanations: many of the material incentives to which actors might be responding will derive from the larger institutional, economic, and political setting in which they are operating. In this sense as well, effective process-tracing of ideational effects must shuttle between levels of analysis. A sole focus on macro-level structures and processes will tend to render ideational effects invisible; a sole focus on individual decision-makers may overemphasize their self-described motivations and occlude the objective constraints under which they were operating.
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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 | 0.045 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".