New Perspectives on the Interplay of Networks and Culture
Bibliographic record
Abstract
Networks and Culture represent two foundational theoretical lenses through which scholarship in organizational theory has historically understood and analyzed organizational phenomena. The former focuses on the patterns and formal properties of social relationships in which organizations – or actors within them – are embedded; the latter on the sets of meanings, local practices, repertoires, narratives, beliefs and norms that organizational actors create, adopt and use. While most contributions within organizational theory that consider networks and culture as their explanatory lenses tend to privilege one or the other in isolation, several recent studies situate themselves at their nexus, considering how they mutually constitute each other in organizational contexts, or how their interaction can illuminate our understanding of a host of organizational phenomena. This symposium proposes the presentation of four scholarly papers, each of which examines a facet of the interplay between networks and culture, and brings together scholars of organizational theory, sociology, and strategy whose work reflects a theoretical interest within this space. It aims at advancing scholarly conversation and promote theoretical synthesis in organizational research in this area, exploring, in particular, the agentic mechanisms that individuals and organizations devise in navigating the opportunities or the constraints configured by the cultural and social structural relational spaces in which they are embedded. Valuation of Distinctiveness of Visual Culture: The Case of Modern South Asian Art Author: Mitali Banerjee; McGill U. - Desautels Faculty of Management Author: Shreeansh Agrawal; The Brattle Group Who Can Afford to Be Different? Social Structural Contingencies of Unconventionality Premia Author: Pietro Bonaccorsi; U. of Toronto, Rotman School of Management Novelty Evaluation and Framing Strategies: The Outsider Challenge Author: Gino Cattani; New York U. Author: Denise Falchetti; George Washington U. Author: Simone Ferriani; U. of Bologna When are Managers Needed? How Culture and Coordinative Complexity Predict the Evolution of Reporting Relationships in Organizations Author: Danyang Li; U. of California, Berkeley Author: Julien Clement; Stanford U. Author: Sameer B. Srivastava; U. of California, Berkeley
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".