Bibliographic record
Abstract
Organizations need morally good people. The field of management has a fundamental mandate to present clear guidance to managers on how to understand, assess, and measure individuals in terms of their ethical or moral qualities. I present two metaethical theory-building contributions. First, I introduce the assessment inference model, a multi-layer framework that illustrates the normative-empirical process of assessment. The framework gives due attention to the distinctiveness of the ethical domain and its inferential relationships with the empirical domain. Second, I construct the theory of embodied metaethics, a descriptive theory which establishes moral goodness as a concept that is pluralistic, intersubjective, and contingent due to the diversity of relations and contexts within organizations. By applying philosopher Maurice Merleau-Ponty’s phenomenological perspective of embodiment, I introduce metaethical dimensions that help us to typologize eight paradigms of moral goodness. I improve upon the limited view of moral goodness currently acknowledged in ethical and management theory, fundamentally changing the scholarly and professional discourse about individual ethics and the role of assessment in ethical management 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.
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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".