Bibliographic record
Abstract
This paper takes its point of departure from the observation that the language of corruption in democratic politics is pervasive — even in democracies regarded as relatively clean in global terms, such as (USA, Sweden, Canada). Charges of “corruption” regularly appear in the press — in commentary, in editorials. Public opinion surveys show that much of the public regards “politics” as in democracies as “corrupt.” Political scientists tend to view these findings as evidence of disaffection from politics generally, rather than any particular pathology that could be addressed through institutional change, reform, transparency, or other fixes. Indeed, because charges of corruption can rarely be evidenced — overt corruption is the exception rather than the rule — our tendency has been to treat such charges as indicators of other issues, such as citizen disaffection.I take charges “corruption” at face value by pursuing the common everyday meanings of such charges: citizens believe politicians dissimulate, they lie, they “serve themselves” rather than their constituents, they are insincere, and they are disingenuous. They speak in meaningless generalizations, or use formulas that evoke broad values while avoiding commitments. What is behind such charges, I suggest, is an everyday intuition into the centrality of promise and commitment in democratic politics. Indeed, this feature of democracy is its defining characteristic: it is what allows “politics” — collective decision-making and action in the face of conflict — to be conducted through talk rather than other possible means — coercion, tradition, or purely economic incentives. My key argument in this paper is that it is common for this defining medium of democracy to be “corrupted” — in the literal sense that agents fail, often strategically and even deceptively, to follow through the commitments implied in language use. Pragmatic approaches to language use shows that such commitments — “deontic scorekeeping” in Robert Brandom’s terms — are essential to the force of any linguistic act. If we understand this feature of language within the context of talk-based politics — conflict and strategy conducted through speech — we can see that democracy is particularly prone to the “corruption” of the commitments implied in language use. It follows that the key problem for democratic theory and practice is to find the mix of ethics, incentives, and institutions that protect this element of talk — commitment — from the intrinsic hazards of politics. This framing of the problem yet another way of understanding the project of deliberative democracy: It is comprised of institutions and practices which hedge against linguistic corruption.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".