Legal guide to emerging technologies
Bibliographic record
Abstract
"Technological progress has never been more rapid, complex or innovative. Legal counsel and business leaders face the ever-growing challenge of not only staying up to date with the latest technological developments, but also navigating the legal implications associated with their adoption. New ways of transacting and interfacing with the world are driving questions about protecting users and creators alike. Understanding and navigating emerging technologies is now important for all businesses - even if an organization is not developing technology, it is certainly procuring or otherwise relying on it. The purpose of this text is to serve as a one-stop shop for legal counsel and business leaders as they navigate the growing involvement of emerging technologies. This practical guide provides an overview of emerging technologies that are poised to drive many changes in the upcoming years: biometric data and technology, autonomous vehicles, Internet of Things, generative artificial intelligence, the metaverse, and NFTs. It covers the various legal implications associated with these emerging technologies, outlines the legal framework in Canada as well as in select foreign jurisdictions, and provides best practices for legal counsel. Also included is an easy-to-follow list of best practices when engaging with the particular emerging technology."--
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".