Bibliographic record
Abstract
Political theorists study the meaning of political feasibility in part to better understand the role of empirical political science in normative political theory. In the conventional understanding, political feasibility refers to the ability of an individual or collective agent to bring about a certain political state of affairs. This way of thinking about political feasibility corresponds with the ordinary language use of the term, but it asks empirical political science to carry the very heavy burden of assessing the likelihood that a plan of action will bring about a particular future state of affairs. In this article I develop a different way to think about political feasibility as part of a conversation about the meaning of the future. Building on Gadamer’s view of historical interpretation, I argue that empirical political science can be understood as creating “history of effects” towards possible futures as a way to enable understanding of future meanings. I use this framework to examine the place of arguments about feasibility in the processes of reason giving that take place in the public sphere. I do so by interpreting the “can” in the principle of “reasons that all can accept” as referring to an interpretive horizon of what can become feasible.
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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".