Appraising longitudinal trends in the strategic risks cited by risk managers in the international water utility sector, 2005–2015
Bibliographic record
Abstract
We report dynamic changes in the priorities for strategic risks faced by international water utilities over a 10year period, as cited by managers responsible for managing them. A content analysis of interviews with three cohorts of risk managers in the water sector was undertaken. Interviews probed the focus risk managers' were giving to strategic risks within utilities, as well as specific questions on risk analysis tools (2005); risk management cultures (2011) and the integration of risk management with corporate decision-making (2015). The coding frequency of strategic (business, enterprise, corporate) risk terms from 18 structured interviews (2005) and 28 semi-structured interviews (12 in 2011; 16 in 2015) was used to appraise changes in the perceived importance of strategic risks within the sector. The aggregated coding frequency across the study period, and changes in the frequency of strategic risks cited at three interview periods identified infrastructure assets as the most significant risk over the period and suggests an emergence of extrinsic risk over time. Extended interviews with three utility risk managers (2016) from the UK, Canada and the US were then used to contextualise the findings. This research supports the ongoing focus on infrastructure resilience and the increasing prevalence of extrinsic risk within the water sector, as reported by the insurance sector and by water research organisations. The extended interviews provided insight into how strategic risks are now driving the implementation agenda within utilities, and into how utilities can secure tangible business value from proactive risk governance. Strategic external risks affecting the sector are on the rise, involve more players and are less controllable from within a utility's own organisational boundaries. Proportionate risk management processes and structures provide oversight and assurance, whilst allowing a focus on the tangible business value that comes from managing strategic risks well.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".