The Driving Force of Institutions: The Co-constitutive Role of Values and Emotions in Institutions
Bibliographic record
Abstract
There are flourishing streams of research on emotions and on values in institutions. Yet most often, these forces are considered separately, focusing on emotions or values in institutional processes. This siloed approach misses the critical, complex and dynamic link between emotions and values. On one hand, emotions fuel engagement with or resistance towards institutionalized values; on the other hand, values shape and regulate emotional bonds. When we talk about admirable and admiration, shameful and shame, disgusting and disgust, the emotional experience and evaluative properties are inherently tied together. Emotions have important moral relevance and values have deep emotional implications. At times, what we experience as emotions and values align, creating an exponentially powerful force well beyond the scale of each of the two factors alone; at other times, emotions and values clash and move us in unexpected directions, driving institutional change and transformation. In this Symposium, we bring together a panel of experts in institutional theory to discuss the critical relationship between emotions and values, considering what they bring to our lived experience, and how, together, they are so much more than the sum of their parts.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".