Bibliographic record
Abstract
Governments, corporations and organizations must implement risk assessment and risk management policies to combat coronavirus in the short term, but also in the long term. Risk assessment will produce two potentially competing calculations: (1) Risk to health and safety from the virus; and (2) Risk to the economy from the cancellation of events, remote work, and impacts on the stock market and banking. This is where the problem starts. In the early stages, governments and organizations will identify risks to both health and the economy, but may lack direction on how to prioritize these two sectors. This article argues that risk to health must take priority over risk to the economy for legal and philosophical reasons. First, section 217.1 of the Canadian Criminal Code requires that organizations take reasonable steps to prevent harm to workers and consumers arising from work. Secondly, applying the work of John Rawls in A Theory of Justice, from behind the veil of ignorance it can be asked what rules would you choose, not knowing whether or not you might fall into the class of persons more vulnerable to suffer serious consequences or even death from coronavirus? The first rule that you would choose is that regulatory measures must promote human health and safety as a first priority. Applied to industries such as telecommunications, government regulation should ensure that there is the equivalent of an “emergency lane” on broadband networks, for health, safety and security-related traffic; Government policy should attract investment in the market that will maintain and enhance the infrastructure for the “emergency lane” in the future. Social and economic inequalities in any “pay for priority” internet fast lanes are just only if they result in compensating benefits for everyone, and in particular for the least advantaged members of society.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".