Age Related Ethical Lapses in Construction Engineering Site Management Decisions
Bibliographic record
Abstract
Risk chasing can erroneously lead to suboptimal decisions. On construction sites, the pursuit of saving time and money can place construction managers in situations where ethics are involved. In today’s integrated construction sites, younger and more novice construction professionals are increasingly required to make quick decisions on vital matters. Although the impact of age on risk chasing has been thoroughly studied in the behavioral economics literature, a gap was identified in how age affects the propensity of ethical decision making with a key emphasis on construction sites. Based on this knowledge gap, an experiment was developed to compare two groups: (1) one hundred college engineering students with some limited project experience, and (2) forty-eight highly experienced construction leaders. Responses to a set of questions framing a situation common on typical construction sites are compared between the two groups. The findings suggest that the younger respondents are more likely to pursue options that, although they save the project money, are nonetheless unethical. The findings can be used by construction and engineering management professionals to help understand and characterize site decision making behavior, and to support the development of training tools to mitigate the costs associated with unethical and suboptimal decisions.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".