The assessment of individual moral goodness
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 In a field dominated by research on moral prescription (business ethics) and moral prediction (behavioral ethics), there is poor understanding of the place of moral perceptions in organizations alongside philosophical ethics and causal models of ethical outcomes. As leadership failures continue to plague organizational health and firms recognize the wide‐ranging impact of subjective bias, scholars and practitioners need a renewed frame of reference from which to reconceptualize their current understanding of ethics as perceived in individuals. Based on an assessment and selection perspective from the field of human resource management, an alternative to conventional deductive‐prescriptive approaches is proposed based on a pluralistic concept referred to as moral goodness. An inductive‐descriptive theory‐building framework is constructed based on three interrelated streams of inquiry to yield insight concerning both formal and informal instances of assessment. Recommendations are proposed for the application of the framework to future research and practice.
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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it