Bibliographic record
Abstract
I argue that while Amartya Sen provides good reasons for focusing on the agency aspect of individuals, his account is too constrained.Sen describes agency in terms of individuals and their capacity for rationality and fails to account for the effect of a broad network of relationships.I turn to Sarah Clark Miller's expanded account of agency to argue that the abilities of relationality and emotionality are also relevant for effective agency.I then argue that Miller's focus on the moral duty to care means that her account is also too individualistic.I ground her account instead in the thoroughly relational nature of agents to show that her account must be coupled with a relational account of autonomy to be adequately sensitive to the effects of oppression on agents.I end by suggesting that oppressed agents can challenge social conditions that are not completely conducive to full autonomy through solidarity.'philosophy' but I heard no such judgment.I think that no matter what I chose to do they would be behind me; they're just those sort of people.It was also very helpful to have v parents (well really, I'm looking at you mom) who worked hard to be financially supportive so I didn't have to worry so much about those things.To Mom: I could not be the independent, intelligent, thesis-writing person I am today without your constant love and support throughout my life.At the end of the day, when it seemed like no one else could totally understand what it is I needed, I went to you.You may not always feel appreciated but please know that you are.When it seems like I don't need you it's only because you did such a good job in the first place.To Dad: I know a lot of who I am comes from you even if sometimes I don't want to believe it.I think it's a lot of the good parts of me too.I know you were there for me in the ways you knew how to be.(And thankfully mom was around for when that wasn't enough!)To Evan: It was always great for my confidence to have someone looking up to me (even if you would never admit it!)I like to think I helped a little bit in you becoming the person you are.I am so proud of you and think so much of our individual success comes from each other.I needed a little brother and I like to think you needed me too.You three have played a crucial role in helping me become the person I am today.I love you.Thank you.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".