Bibliographic record
Abstract
Drug molecules are the centrepiece of modern medical therapies, providing relief from pain, combatting infections and providing a myriad of other therapeutic effects. The quest for new and improved drug molecules drives medical research, and the introduction of a new drug frequently becomes a newsworthy event capturing the attention of the press and general public. And yet, misconceptions abound. Often, the general public thinks that drug molecules are designed, created, and invented by physicians rather than chemists - a misunderstanding that is merely one aspect of a widespread general underappreciation of the role of chemistry in the health and socioeconomic well-being of humankind. Chemistry as a discipline needs to change this narrative. Our journals, conferences, societies, mass media presence and social media postings need to better inform the general public about the societal value of chemistry. Though it is an arduous and time-demanding process, chemists, both in academia and industry, invent the drugs that are advancing medical care. We chemists need to do a better job educating policy makers, politicians, opinion leaders and fundraisers about the valuable contributions of chemistry. We need to have people know what we do, and why we became chemists; we need to engage the general public.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".