Credible Judgment: Combining Truth, Beauty, and Justice
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
Abstract The research summarized in this chapter provides descriptive evidence to support House's vision of validity by expanding connections with his theory to a wide variety of professions, in addition to professional evaluators. Perhaps these results and discussion of them and the emerging model will invite professionals to reflect upon ways to improve their own evaluative judgments. Case study interviews were conducted in Canada and the United States with 27 professionals from many helping professions, including law and law enforcement, social work, medicine, education, business, sports, and chaplaincy. Participants were asked to discuss examples of successful and less successful evaluative judgments they had made in their professional work. Citing patterns discovered through analysis of these contrasting examples, we linked their experiences to House's framework regarding truth, beauty, and justice as foundations for validity. This research thus generated a descriptive model of a process to produce credible evaluation judgments with six interacting elements: (1) credible judgments evolve through an iterative process; (2) frameworks, protocols, and methods may help professionals generate valid evidence, but they are often not sufficient; (3) stakeholders’ involvement is essential, and how they participate varies depending on the circumstances; (4) the path required to generate a credible judgment is rarely linear; (5) credible judgment is based on strong argumentation that is properly developed and aesthetically presented; (6) the production of credible judgments depends on special dispositions, orientations, or qualities of the professionals.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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