Bibliographic record
Abstract
Abstract This paper is the text prepared for the keynote address of the EUSN 2017 conference in Mainz, Germany. A short presentation of concepts reflects in part the foundations of neo-structural sociology (NSS) and its use of social and organisational network analyses, combined with other methodologies, to better understand the roles of structure and culture in individual and collective agency. The presentation shows how NSS accounts for institutional change by focusing on the importance of combined relational infrastructures and rhetorics. Specific characteristics of institutional entrepreneurs who punch above their weight in institutionalization processes are introduced for that purpose, particularly the importance of multi-status oligarchs, status heterogeneity, high-status inconsistencies, collegial oligarchies, conflicts of interests and rhetorics of relative/false sacrifice. Two empirical examples illustrate this approach. The first case focuses on a network study of the Commercial Court of Paris, a 450-year-old judicial institution. The second case focuses on a network study of a field-configuring event (the so-called Venice Forum) lobbying for the emergence of a new European jurisdiction, the Unified Patent Court, and its attempt to create a common intellectual property regime for the continent. For sociologists, both examples involve “studying up”: they are cases of public/private joint regulation of markets bringing together these ingredients of institutionalization. The conclusion suggests future lines of research that NSS opens for the study of institutionalization, in particular using the dynamics of multi-level networks. One of the main issues raised by this approach is its contribution to the study of democratic deficits in a period of intense institutional change in Europe.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".