Bibliographic record
Abstract
In defending the principle of neutrality, liberals have often appealed to a more general moral principle that forbids coercing persons in the name of reasons those persons themselves cannot reasonably be expected to share. Yet liberals have struggled to articulate a non-arbitrary, nondogmatic distinction between the reasons that persons can reasonably be expected to share and those they cannot. The reason for this, I argue, is that what it means to “share a reason” is itself obscure. In this paper I articulate two different conceptions of what it is to share a reason; I call these conceptions “foundationalist” and “constructivist.” On the foundationalist view, two people “share” a reason just in the sense that the same reason applies to each of them independently. On this view, I argue, debates about the reasons we share collapse into debates about the reasons we have, moving us no closer to an adequate defense of neutrality. On the constructivist view, by contrast, “sharing reasons” is understood as a kind of activity, and the reasons we must share are just those reasons that make this activity possible. I argue that the constructivist conception of sharing reasons yields a better defense of the principle of neutrality.
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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".