Speaking about vision, talking in the name of so much more: A methodological framework for ventriloquial analyses in organization studies
Bibliographic record
Abstract
Organizations have long been treated as stable and fixed entities, defined by concrete buildings, catchy names, and strategic goals neatly written on paper. The Communicative Constitution of Organizations (CCO) school proposes an alternative, practice-grounded conceptualization for studying organizations as emerging in communicative (inter)actions. In so doing, CCO invites organizational scholars to trace back organizational phenomena to how they are communicated into existence. The concept of ventriloquism can help us explain the communicative constitutive view as it depicts how various elements of a situation are communicated into being and make a difference in interaction. However, ventriloquism lacks a proper methodological outline. Taking employee conversations about visions—a classic constituent of organizations—as our venue, we created a four-step framework for ventriloquial analyses and explored how visions are talked into existence. In this paper, we introduce and illustrate our analytical framework, showing how to identify, order, and present ventriloquial effects. We thus provide organizational (communication) scholars with a new methodological tool that facilitates the systematic inquiry into organizing and the organized from a communicative constitutive perspective.
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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".