Bibliographic record
Abstract
In July 2024, a global outage in the software provided by the US cybersecurity company CrowdStrike caused major disruptions to flights, health services and payment systems. Although the outage resulted from a simple software patch update, the event underscored the significant risks that software failures pose to essential services. Several commentators described the outage as a prime example of the dangers associated with the diffusion of AI and its application in critical areas of the economy, prompting renewed calls for AI guardrails. Such calls for regulation are not new. For instance, more than 1,500 engineers and computer scientists (including Elon Musk and Steve Wozniak) previously called for a six-month pause in the training of AI systems to ensure the technology meets adequate safety standards. Policies that slow down the development and diffusion of new technologies are likely to influence welfare through multiple channels and generate complex trade-offs. In this context, examining the economic effects of the rate of AI diffusion, as conducted by Gans (2025), is an important, timely, and policy-relevant exercise.
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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.084 | 0.034 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".