Bibliographic record
Abstract
To the Editor: Tom Curran produced a delightful article on “translational research” (1). Identifying problems in our current strategies to combat cancer may lead to positive changes. Dr. Curran mentions that “instead of rewarding teamwork, we teach competition and suspicion and we create barriers to collaboration” and that “we need to encourage teamwork, cooperation, and open communication.” Under the current system of funding and rewarding achievements, this wish cannot be fulfilled. How could a postdoc, working in a mega-lab with 30 to 40 others, hoping to get a paper in a high-profile journal, be cooperative and collaborative? Not a chance!The way the current system works, it seems that as scientists become more successful, their chances to “cure cancer” are decreased. Those who devoted their life to cancer research started with a dream of finding a cure. This enthusiasm is progressively eroded as one gets more successful and enjoys the associated rewards. The “dream” is slowly transformed into a “business” with goals that are not necessarily focused on “cures.” In my opinion, the problem is that as success increases, quality time for creative thinking, reading literature, spending time in the lab, etc., decreases. It goes like this, you publish good work, you get more invitations to speak, fly a lot, write more papers, apply for more grants, receive requests for consultation in exchange for money and stock, become Associate Editor of prestigious journals, reviewer of grants, participate in committees, and on it goes.Once your lab hits 20 to 30 people, you do not remember their names. Incoming journals stay unopened for weeks and you have no time to answer your e-mails. At the end, you meet with your students in the corridor. Your name can attract mega-projects and millions of dollars. You hire professional grant writers, “second-in-command,” etc. At the end, you become a “celebrity” and have your own company. You have too much to think of in the morning and finding a cancer cure becomes a detail. Celebrity status can still get you large grants and papers, but you may not know exactly what they say.Why aren't too many young people are interested to follow a research career? Those who are exposed to science as undergraduate and graduate students realize that this is a tough and very competitive profession. Striving to publish articles and obtain grants and getting early slaps in the face is no fun. I have heard complaints from M.D. to Ph.D. students that their much needed imagination is entrapped very early in their careers by trying to get fast results and publications, that, they hope, will help them to get grants.How could we have hopes that cancer patients will get better treatments in the future? One solution is for granting agencies and associated bodies to focus on the youth. Here are some suggestions:Investing in youth will bring in many more bright minds into science. Along with them will come new ideas and approaches and, likely, more innovative cancer cures. Although the necessity of funding mega-projects and mega-centers is not questioned, the brightest ideas are likely to come from individual gifted minds who have time to think, not from frequent flyers and busy celebrities.
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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".