Bibliographic record
Abstract
Experiences of disorientation can be common and powerful parts of moral agents‘ lives, yet they have not been characterized by mainstream Western philosophers, and their effects have not been adequately recognized by ethicists. In this dissertation, I remedy these gaps by providing an account of disorientations as multi-dimensional experiences and by fleshing out a more nuanced analysis of disorientation within the framework of experienced agency. I argue that, contra the philosophical tradition, disorientations are not always bad for moral agency.\nThis thesis has two main aims: first, to introduce a philosophical framework to clarify experiences of disorientation and their effects; and second, to clarify the relation between disorientation and moral agency, showing how responsible action can both require and produce disorientation. In chapter one, I introduce disorientations as complex experiences of unease, discomfort, and uncertainty which vary in degree and in effects. In chapters two to four, I characterize disorientations on three axes: corporeal, affective, and epistemological. I argue that disorientations always involve all three dimensions of bodily, emotional, and cognitive experience and that shifts in body, affect, and knowledge can trigger experiences of disorientation. I draw on examples of how agents can become disoriented in periods of illness, trauma, grief, self-doubt, and education. In chapter five, I draw two lines of connection between disorientation and moral agency: experiences of disorientation can help us act more responsibly, and acting responsibly can be disorienting. In chapter six, I consider the political promise of disorientations, focusing on the way individuals‘ disorientations in response to a hate crime in their community prompted the creation of less harmful norms, and thereby a better place for individuals to live. In chapter seven, I conclude by outlining implications of my view for how we should face disorientations and what kinds of conditions should be in place to support those who are disoriented.\nDisorientations do not always enable moral agency. Given that moral philosophers are better versed in the ways disorientations can harm, my project is to distinguish the ways they can help, contesting the assumption that moral agency is always better the more oriented we are.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".