The Watergate Effect: Or, Why Is the Ethics Bar Constantly Rising?
Bibliographic record
Abstract
This chapter begins with a paradox. In most of the “old” democracies, there has been in the past years growing concern about the ethics of public officials. But at the same time, empirical evidence of unethical behavior in the political sphere does not suggest an increase. As a former Canadian Ethics Counsellor has argued, the “ethics bar,” in terms of rules and standards of conduct, is “constantly rising,” but “in the real life,” instances of “ethical lapses are relatively uncommon” (Wilson 2002, 2). In his Ethics in Congress , Dennis Thompson (1995) similarly noted that even if there is “escalating concern about ethics” in Washington, “there is no evidence that the character of members in recent Congress is worse than their predecessors … it may indeed be better” (3–4). A study published in 2002 by the Brookings Institution comes to the same conclusion: “Worry about the ethics of public officials greatly exceeds formal evidence of ethical violations” (Mackenzie 2002, 98). In many countries, the last decade or so has witnessed the steady accumulation of ethics regulations and the expansion in strength and scope of organizations involved in enforcing standards of conduct in public life (Gay 2002). This raises the question: Why is the ethics bar constantly rising? The literature on public ethics in political science, which is mostly atheoretical and normative, offers little guidance when attempting to answer this question. Much of the scholarly focus on political ethics can be broadly divided into two perspectives.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".