Bibliographic record
Abstract
Increasing legislation and use of cannabis across the U.S. demands thoughtful, scientifically sound, practical responses from states and communities. To ensure such responses, “best of the best” minds will come together in an objective national platform to facilitate frank discussion of policies and regulatory approaches, emerging research, and prevention and treatment approaches. Advocates for Human Potential will convene the 2019 National Cannabis Summit: Science, Policy, and Best Practices (Summit 2019), expanding on the first national forum (2017 National Cannabis Summit) to explore science, policy, regulatory, and governance issues of cannabis legalization using a research and public health approach. Summit 2019 will be held in California, long at the forefront of legalizing medical and recreational cannabis use, and is planned with input from the University of California Presidents Office; California Department of Public Health; California Bureau of Cannabis Control; and University of California, Los Angeles Integrated Substance Abuse Programs. In addition, involvement of the Canadian Centre for Substance Abuse will lend an international perspective. This 3-day scientific conference is designed for public health professionals in research, policy, regulatory oversight, and enforcement, as well as practitioners in public health and clinical services. Summit 2019 will maximize learning through original, informative, and engaging keynote sessions on the scientific, clinical, and political landscape surrounding state legislation of marijuana. Breakout workshops targeting specific issues will be organized around six tracks: emerging research and epidemiological data; governance, federal law, and emerging policy; prevention and education; health effects; public health and public safety; and regulatory issues. Networking and discussion will be facilitated through informal and interactive lunch-and-learn sessions, as well as poster presentations, during which authors and researchers interact with attendees. Extensive use of technology, including customized software and a social media presence before, during, and after, will exponentially increase the range of information sharing. Grounded in balanced and objective science-based perspectives, Summit 2019 will not include an exhibit hall and have no ties to the marijuana industry.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.152 | 0.064 |
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".