Bibliographic record
Abstract
: Globalisation has increased our awareness of crises and their impact on our lives. It is, therefore, more important than ever for governments to respond to crises and to communicate with target groups and the public at large. This article examines the theoretical bases of decision-making in organisations to consider the requirements of an effective crisis communication decision-making process in an evolving public sector organisation such as the Government of Canada. It begins with an overview of the scope of crises, followed by an examination of the management issues that crises raise, specifically in relation to organisational decision-making. The new public organisation as defined by Kernaghan, Marson and Borins (2000) represents the organisational outcome of the trends that are forcing public sector organisations to change from a hierarchical to a more horizontal form of management. An analysis of their model is undertaken to assess whether the leadership/decision-maker competencies that are required for decision-making in this environment meet the management and governance challenges of the evolving Westminster bureaucratic organisation. It also analyses Rosenthal and Kouzmin's (1997) five-step heuristic model to determine whether it reflects the governance challenges and the leadership competencies required for effective decision-making in the Canadian public sector. Building on the analyses of these two models, it is proposed here that, as a result of the Government of Canada's ongoing evolution towards the new public organisation, three core interrelated competencies (the abilities to manage information, to think horizontally in a changing management environment, and to deal with authoritative ambiguity) are required by managers to facilitate effective crisis communication decision-making. It is against these three core interrelated competencies that the Government of Canada document Government of Canada Framework for Public Communications Management of National Security Threats, which outlines a public sector crisis communication decision-making process, is assessed to determine their applicability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".