Imagining organization through metaphor and metonymy: Unpacking the process-entity paradox
Bibliographic record
Abstract
Within organization studies, Morgan’s seminal book Images of Organization has laid the groundwork for an entire research tradition of studying organizational phenomena through metaphorical lenses. Within Morgan’s list of images, that of ‘organization as flux and transformation’ stands out in two important regards. First, it has a strong metonymic dimension, as it implies that organizations consist of and are constituted by processes. Second, the image invites scholars to comprehend organizations as a paradoxical relation between organization (an entity) and process (a non-entity). In this article, we build on Morgan’s work and argue that flux-based images of organization vary in their ability to deal with the process-entity paradox, depending on the degree to which its metaphorical and metonymic dimensions are intertwined. We also examine three offsprings of the flux image: Organization as Becoming, Organization as Practice, and Organization as Communication. We compare these images regarding their metaphor–metonymy dynamics, the directionality of their process of imagination, and their degree of concreteness. We contribute to Morgan’s work, and to organization studies more generally, by offering an analytical grid for unpacking different processes of imagining organization. Moreover, our grid helps explain why images of organization vary in their ability to comprehend organizations in dialectical and paradoxical ways.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.006 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".