Lost in translation: inadequate non-technical risk assessment within major project teams in mining
Bibliographic record
Abstract
Infrastructure projects increasingly encounter delays due to non-technical risks (NTR), those risks arising from interactions between business and external stakeholders with the potential to create future negative impacts on society and the environment. One sector where NTR is having a significant adverse impact is the global mining sector, where industry leaders rank NTRs as the leading cause of business risk. We investigate how NTRs are assessed during project pre-feasibility using semi-structured interviews with 20 respondents from major mining companies. We find four main factors contribute to the problem of NTR assessment: there is lack of clarity about what constitutes a NTR; there are different interpretations of how NTR is defined and evaluated; there are disciplinary silos within project teams that impede a holistic assessment of risk; and there is conflation between risk and root cause. These factors contribute to striking differences in perceptions of non-technical risks between professionals in project management versus their sustainability colleagues. A four step process is proposed to improve non-technical risk assessment, align project and sustainability professionals, and identify opportunities for mitigation measures. This work seeks to improve NTR management within mining, a sector that is under-represented in existing literature, by adding empirical research examining how project teams identify and assess non-technical risk and contributes to theory at the nexus of project management and sustainability.
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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 | 0.068 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".