Public Integrity. By J. Patrick Dobel. Baltimore, MD, and London: Johns Hopkins University Press, 1999. 260p. $38.00.
Bibliographic record
Abstract
Integrity is a shifty, furtive concept. Philosophers have had a hard time defining the idea because it raises a couple of recurrent perplexities. First, consider former Speaker Jim Wright's remark that "integrity is . . . the state or quality of being complete, undivided, [and] unbroken," or the Oxford English Dictionary connotation of an "unbroken state" of "material wholeness." The problem is that integrity, so understood, seems to leave no room for the possibility of individuals whose lives display any kind of self-critical revi- sion, changes in course, or discontinuities over historical time, or for those who compartmentalize, differentiate, and assume conflicting roles across social space; in other words, for all of us. We need, as Amelie Rorty has written ("Integ- rity: Political, not Psychological," in Alan Montefiore and David Vines, eds., Integrity in the Public and Private Domains, 1999), a far better account as to how and where "integration and integrity . . . coincide".
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.181 | 0.155 |
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".