Beyond Ethos: Outlining an Alternate Trajectory for Emotional Competence and Investment
Bibliographic record
Abstract
The paper by Voronov and Weber (2016) moves this conversation to a higher level, theorizing beyond the simple (though important) idea that emotions occur and matter in social life, to a more fundamental engagement of emotions as a defining aspect of "institutional actorhood".We start by acknowledging the important contribution provided by the paper with its compelling introduction of the ideas of "emotional competence" and "emotional investment".Emotions, Voronov and Weber argue, are "institutionally conditioned and thus endogenous to institutional orders" (2016: 5), and emotional competence enables people to perform prescribed roles and inhabit institutional orders.Such competence leads to emotional investment.Voronov and Weber argue that "institutional ethos" is the basis of emotional competence.We take issue with this characterization of ethos and its relationship to the ideas of emotional competence and investment.For us, the ethos concept is confusing and, perhaps more importantly, unnecessarily detached from more established concepts in the institutional literature.This detachment not only adds to the "conceptual muddle" (Colyvas & Jonsson, 2011: 27) of institutional theorizing, but risks undermining the important contribution that emotional competence might make if linked to a more fruitful avenue of future research.We suggest an alternative framing -namely, connecting emotional competence to the more established concept of "institutional logic".Doing so connects emotional competence and emotional investment to the values that are embedded within institutional logics (Dunn & Jones, 2010;
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".