Bibliographic record
Abstract
After walking my younger kids to school, I mounted the Vancouver SkyTrain at Sapperton Station on the morning of Monday December 14, 2009. As always, I took my current reading out of my backpack – on that day the book Effortless Action by Edward Slingerland (2003a), which analyzes the metaphorical nature of Confucian thought. I began thinking about what I read; my mind was wandering when all of a sudden an insight struck me: What Confucius wrote some 2500 years ago is critical feeling. Much has been written about critical thinking, but to my knowledge not one scholar has ever written a comprehensive work about how feelings can be used to improve personal or societal outcomes. The deficiencies of critical thinking have also been extensively covered, and recent decades have seen an increasing number of works on the rationality of emotions. Despite these insights, we lack an overview of strategies that realize the potential of feelings to improve outcomes. Feelings go beyond emotions and encompass moods, preferences, metacognitive experiences, and bodily states, as will be defined in due course. This book introduces the concept of critical feeling and provides an overview of applications in various areas, from personal well-being and skill learning to the acquisition of artistic tastes and religious creeds. Writing such a book is often a solitary affair but at the same time impossible without a host of colleagues and friends who take time to collaborate, discuss, criticize, and encourage. Within the five years since my decisive aha-experience, I have had the privilege of working with and discussing ideas with many people, some of whom I would like to mention by name. At the University of Bergen and later at the University of Oslo, I met wonderful colleagues and students who gave input from various perspectives relevant to the project; among these people were Michael Stausberg (who provided input on parts of Chapter 10), Morten Brun, Kevin Cahill, Per Olav Folgerø, Marina Hirnstein, Lasse Hodne, Sigve Høgheim, Kenneth Hugdahl, Christoph Kirfel, Geir Overskeid, Francisco Pons, Ole Martin Skilleås, Karsten Specht, and Matthias Stadler. Some of my research relevant to critical feeling has been made possible by grants from the Research Council of Norway (#166252 and #212299) as well as by a fellowship from the Leiv Eirikssons mobility program.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.448 | 0.265 |
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".