Bibliographic record
Abstract
Government regulation of individual and business activity is part and parcel of modern society. But many businesses face difficulties in understanding and navigating the legal hurdles, rules, and uncertainty that come with modern regulation. Many governments in Canada have taken steps to reduce this burden by streamlining regulation and cutting unnecessary red tape. In this Commentary, I explore how regulators can continue this trend toward more efficient and effective regulation: by embracing data analytics and machine-learning tools. Big data, analytics and machine learning offer new and difficult challenges for regulators who oversee how many businesses make decisions. But regulators can also benefit from effective use of data science. Some of these benefits can be realized almost immediately by using data that the regulators already have. First, regulators can better predict who should and should not be investigated. A regulator needs to make choices about how to allocate and prioritize scarce resources. With the right data and appropriate data analytics, predictions can be made about where to best place investigation resources. Second, regulators must make choices over which cases to prosecute. Regulators should not waste resources litigating cases they are likely to lose. Instead, regulators should put resources only toward cases that they are likely to win. Regulators can turn to the data and use machine learning to predict how a court would resolve a particular problem. Moving further into the future, big data and machine learning will change the way that laws and regulations will be consumed and produced. Lawmakers will have greater ability to provide relevant information before the individual or business acts, rather than waiting to adjudicate after they have acted. Businesses will seek prior authorization for many more regulated actions. Furthermore, the time and cost for regulators to respond to the queries will fall drastically. Instead of relying primarily on vague guidelines, regulators will be able to offer more expedient and personalized responses. There are enormous benefits to regulators making decisions before individuals and business act. Advance rulings, given before investments are made, provide certain outcomes and reduce the likelihood of wasted investments. There are, of course, a number of potential barriers and issues that may arise. These include: the quality of the data, accountability and due process, the need for transparency, privacy and the reluctance to share data, the benefits of uncertainty, and the stability of social views and goals.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".