Value creation from internalizing non-technical risks in projects : mining sector case
Bibliographic record
Abstract
The mining sector faces both technical and non-technical risks (NTRs). Technical risks are defined as engineering-related risks whereas NTRs emerge primarily due to inadequate management of social and environmental issues. Research shows that mining projects are mostly designed with a focus on technical risks. This is surprising because there is evidence that the main causes of project capital overruns and delays arise from the less tangible non-technical risks, either real or perceived,. Furthermore, industry surveys in both 2019 and 2020 have identified social license to operate as the number one business risk facing the mining sector. Through direct engagement with industry practitioners, this thesis investigates differences in perceptions about the definition of NTRs, considers the extent to which NTRs are currently integrated into project assessments and identifies reasons for the neglect or undervaluation of NTRs in global mining projects. The research is informed by primary data collection in accordance with the grounded theory approach, which was employed for the study design. An exploratory and qualitative approach is adopted, comprising of semi-structured interviews with 20 professionals working for mining companies who are recognized as leading players in sustainable development performance. The research focus is fine-tuned from “infrastructure projects” to “large capital projects” to “mining” to “ESG leaders” to “project stage” (pre-feasibility stage). NTRs are currently undervalued in the decision-making processes of major mining companies. This is concerning. It suggests that projects assessments are not undergoing a complete risk analysis because projects are designed with a focus on mitigating “risks to the project” instead of “risks to people”. Project professionals demonstrate high motivation to integrate NTR into projects appraisals, but they face disincentives from corporate short-termism perspectives, decision bias, lack of expertise and miscommunications across departments. Structurally, Chapter one provides an introduction, Chapter Two discusses the main findings of the literature review, Chapter Three explains the research design and methodology, and Chapter Four presents the research results, Chapter Five discusses and analyzes key data outcomes, and Chapter Six identifies recommendations for decision-makers, and notes both academic contributions and opportunities for future research.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".