Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,045 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,045 |
| Communication savante | 0,012 | 0,030 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».