Multimodality, Meaning, and Institutions
Bibliographic record
Abstract
The insight that institutions, and the communicative practices that create, sustain, and challenge them, are multimodal accomplishments has garnered increasing attention from scholars in organization and management research over the last decade. Traditional understanding of social knowledge and meaning as being constituted primarily through verbal discourse has been challenged and extended by work that has promoted the centrality of visual, material, and other sign systems (e.g., audio, gestures, layout) for constructing social reality. While some discursive approaches to organizations and institutions have acknowledged the existence and relevance of modes other than the verbal for some time, systematic research on multimodality has remained rather sparse. In particular, the interaction and orchestration of multiple modes remains terra incognita with considerable empirical, methodological, and theoretical stakes. Together, 54A and 54B of Research in the Sociology of Organizations investigate these issues with innovative research that focuses on the relationship between different modes in the emergence, diffusion, maintenance, and challenge of social meanings and institutions. Individual contributions demonstrate the potential of multimodal approaches to rejuvenate and extend the study of institutions, they revisit research on classic phenomena in organization theory through a multimodal lens, and advance the design of relevant and rigorous methods of analysis for the study of multimodal communicative practices
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".