The Watergate Effect: Or, Why Is the Ethics Bar Constantly Rising?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
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 | 0.001 | 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 | 0.006 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it