“Giant Toxic Lakes You Can See from Space”: A Theory of Multimodal Messages and Emotion in Legitimacy Work
Bibliographic record
Abstract
Organizations need to appear legitimate to access resources. Thus, actors often carry out legitimacy work to shape others’ evaluation of something as “desirable, proper or appropriate.” Such research has tended to focus on the cognitive appeal of words. Recently, research has also emerged on the persuasiveness of images, especially for creating emotional appeals. We develop a process model to explain the role of multimodal messages—combining words and images—in legitimacy work. With this model, we aim to answer: Why do certain combinations of multimodal messages (words and images) more forcefully evoke emotion and more reliably capture recipients’ attention, motivate them to process those messages, and (re)evaluate the legitimacy of an organization, its activities, and/or its industry? We conclude by discussing theoretical extensions and connections to other methods such as institutional work and values work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".