Understanding Risk: Contributions from the Journal of Risk and Governance
Bibliographic record
Abstract
During recent years, news headlines have been rife with criticisms of the risk management practices of public and private sector entities. These criticisms have often been accompanied by calls for greater transparency in the way government entities manage risks and communicate dangers to the public. Similarly, in the private sector, the internationalisation of economic activity has heightened concerns over the potential adverse implications of mismanagement and financial scandals, and has led to calls for greater regulation and supervision. While the responses of public sector agencies and private sector actors to these challenges have differed, they share a common acknowledgement that effective governance relies on the pro-active identification, assessment, and management of risks as well as appropriate regulatory frameworks. This edited book covers a number of divergent topics illustrating the emergence of several novel themes in the area of economic and social risks. As a communality, these novel themes relate to the complexity in which human activity in this late stage of capitalist development is embedded. This risk-generating complexity, in turn, can be observed at several levels, including workplace hazards, governance problems within the private sector or the intersection between public and private, and in relation to the economic risks faced by larger entities such as national governments.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".