Generative artificial intelligence community of practice for research
Bibliographic record
Abstract
A recent editorial 1 discussed the use of generative artificial intelligence (AI) approaches in the management of wounds.As a researcher in the field of wounds, the author provided some insight into how such systems could benefit researchers in this space.The use of such approaches in communities of practice was discussed, providing some of the benefits of this type of approach to researchers.Generative AI can help researchers understand complexity and provide "accurate" summaries of data and outcomes.It can help provide summaries of individual and multiple studies, helping researchers not only understand the content but also provide the outputs to educate others.These communities of practice can also potentially use generative processes to begin drafting manuscripts, this is the automation of the current research processes today.But more study is required to explore the legal and ethical ramifications of the use of generative systems in peer science, as well as training the logic models to provide peer-validated outcomes.The editorial mentioned that this was not too far in the future, but do you realise it exists today?Such approaches sum up my field of research for the past few decades, where we worked on the proposed delivery of a personalised information directory for YellowPages Canada (YPG), called YellowSpace.YPG was dropping a 1-kg phone book on everyone's doorstep.Why not have a personalised digital version of their phonebook for all YellowPages users and businesses across Canada?This approach is more practical and environmentally friendly.The purpose of YellowSpace was to improve user engagement with the directory listings through behavioural content targeting, providing a unique, personalised view of their national directory for every user across Canada based on their personal preferences and "word of mouth" (family and friends) business logic.We augmented the directory results by grouping the behavioural actions of close friends and family to make recommendations for food, accommodation, travel,
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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 | 0.129 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.042 | 0.038 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.032 | 0.036 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".