Bibliographic record
Abstract
Political philosophy has a lot to say about oppression, but does it adequately address the issue? This project asserts that it does not. The primary goal of this thesis is to redefine how we look at this socio-political problem, and to create a new model for analysis and application. I begin with a discussion of social contract theory and the many ways it has changed in an attempt to properly address the issue of oppression. Following this, the project turns towards an ethico-epistemological analysis of the elements of oppression in the modern social sphere. In this analysis, I look at prejudice, bias, disagreement, virtue, and vice as they pertain to the problem of oppression. Notably, this project considers the epistemic effects/affects of both the oppressed’s and the oppressor’s viewpoint. Finally, the project culminates in the development of the Argument for Self-Skepticism, my alternative to current social contract theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.088 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".