Implicit Values behind Young Architects’ Moral Level: A Case Study in Malaysia
Bibliographic record
Abstract
Moral principles are perpetually of immense significance in human society. Kohlberg has been recognized in the scholar world as the forerunner in identifying moral levels. Though subjective, his six levels of morality set the platform for other researchers to look deeply into it across many parameters. Later on, attempts were also made to measure morality quantitatively. Defining Issues Test (DIT) is one of the most recognized one. Studies went one step deeper with professional ethics being considered as a component of general morality. The challenge was that, while measuring ethics, a universal tool seemed to be unfair to judge different professionals. Moreover, in most cases, code of conducts, instead of morality, was the platform to measure Ethics. Construction-related Moral-judgment Test (CMT) was one of few newly developed tools to measure professional ethics, with ‘construction’ in this case being the profession. This study customized CMT, specific to architects in the context of Malaysia, but adopted Kohlberg’s moral levels as the platform to judge morality, instead of measuring ethical level on the basis of practicing codes of conducts in the profession. Investigating on a sample of 135 young architects around Malaysia selected through stratified random sampling, the study found some implicit interesting factors that emerged. It showed that working experience might be strongly correlated with increasing level of morality, but at young age, it might show a different direction in the curve.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".