Realizing our digital future and shaping its impact on knowledge, industry, and the workforce: G7 Science Academies' Statement 2018
Bibliographic record
Abstract
This statement was prepared by the National Academies of Sciences of the G7 states under the leadership of The Royal Society of Canada to provide scientific advice to the G7 Summit of Heads of State and Governments in Canada in 2018. Digital technologies are transforming the early 21st century, leading to the creation of entirely new industries based upon machine learning and artificial intelligence and lowering barriers to participation in and access to data, education, and communication tools for citizens around the world. It is believed that international cooperation will be essential in key areas of security, accessibility, and regulation to secure a digital future that is inclusive, democratically governed and ethically minded in which open data and reliable information can circulate. With these objectives, the Academies propose the following principles of action: Inclusion and access with the goal of equal opportunity to participate in and gain from the digital transformation, to channel gains equitably and eliminate digital divides. Information literacy relying on a comprehensive educational plan for all age groups with the objective of providing skills and tools allowing citizens to critically interpret, verify and validate the quality of information circulating in the digital infrastructure. Quality of tools and standards through robust mechanisms for production, validation, access and dissemination of open data, information and machine learning systems, to strengthen reliability and security, preventing tampering, manipulation and privatizing use of data and ensuring that machine learning algorithms are interpretable by non-specialists. Democratic governance in the form of regulatory frameworks to set up an oversight of internet service providers, social media and other entities and prevent private monopolistic or oligopolistic power in the digital economy and to ensure open and neutral internet, protection of digital data and respect for norms of individual privacy. Employment and training policies to encourage new economic activities, foster emerging technological sectors and ensure that the benefits of new technologies also be distributed to workers and that schemes be available for their training and reemployment. Ethics and human values should guide the development of digital technologies, artificial intelligence and big data analytics and intervene in all stages of digital innovations to preserve values of freedom, democracy, justice and trust.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.030 | 0.010 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.054 | 0.037 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".