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Enregistrement W2083400358 · doi:10.1373/clinchem.2015.239129

A Day in the Life of Dr. Bean and How the NIH Is Wasting $20 Billion per Year

2015· article· en· W2083400358 sur OpenAlexaff
Eleftherios P. Diamandis

Notice bibliographique

RevueClinical Chemistry · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensMount Sinai Hospital
Organismes subventionnairesnon disponible
Mots-clésWastingGerontologyMedicineDemographyEnvironmental healthInternal medicineSociology

Résumé

récupéré en direct d'OpenAlex

Interviewer: Good morning, Dr. Bean. Thank you for accepting this opportunity to be interviewed. Your comments will be very useful for the new generation of young and upcoming scientists. Given your very successful career, you likely have much to say and lots of advice to give. My objective this morning is to describe one of your typical days. I am sure it will be fun. Should we start? Dr. Bean: Yes, my pleasure. Please go ahead. Interviewer: My interview will be broken into blocks of 2 hours. So, let us start with the first 2 hours of your day. Dr. Bean: Sure, I get up at 6 AM. I first make coffee and eat my breakfast, which brings me to 6:30 AM; then I do my exercise on a treadmill I have at home, finishing at 7:30 AM. You see, at 62, I must do this, otherwise, who knows what might happen. Then, I walk to work and open my office at exactly 8 AM. Interviewer: Sounds great. I guess at this time you are well-rested, relaxed, and ready to go. Dr. Bean: Absolutely! This is the premium time of my day from 8 to 10 AM. And I have strict instructions to my secretary—never book any appointments in this time block. I want this time for myself, for the most challenging part of my job. Interviewer: And what is that? Dr. Bean: Writing grants. You see, now I am working on a rather large NIH grant that is worth $2 million over 5 years. If I get it, it will guarantee about 40% of the lab's budget for a long time. Interviewer: I see; but since this seems to be a rather important issue, do you mind if we go through the rest of the day and then come back to the grants? Dr. Bean: This is reasonable. Let me now cover the 10–12 AM block. This is devoted to various meetings; with my students, my manager, company representatives, etc. Interviewer: Which brings us quickly to noon? Dr. Bean: Yes. At that time I try to catch-up with my e-mail. By noon, I would have at least 50 emails, and a few would require immediate attention. By the time I clear them it is lunch time. And by the time I finish lunch, it is 1:30 PM already. This is the time of the day to make a strong coffee and enjoy it in my office. Interviewer: And the 2 PM block is now ready? Dr. Bean: Yes. This block is reserved for phone calls and more meetings with my students and post-docs. Sometimes we have committee meetings, or deal with University affairs such as teaching etc. On other occasions, I work on my latest manuscripts. Interviewer: And what about the 4–6 PM slot? Dr. Bean: Blocked again; religiously; to continue my work on grants. But we will come back to that in a moment. At 6 PM I have another look at the e-mail just in case there is a burning issue, and by 6:30 PM I am ready to walk back home, dinner at 7 PM, digestion by 8 PM, and work on cleaning out all of my e-mails between 8 and 10 PM. After that, see you tomorrow. Interviewer: But don't you read anything, Dr. Bean? Dr. Bean: Of course I do! Nature and Science, cover to cover, but I keep this activity for when I travel. When I am at airports and on planes, I read, read, read. Interviewer: Ok; but let us go back to grants for now. You seem to be spending about half of your best hours of the day with grants. Do you do this in certain months of the year? Dr. Bean: Yes, 12 months per year. There is no such a thing as I finished a grant; you finish one and you start a new one; it is a continuous, nonstop exercise. Interviewer: Wow! I did not know that. How many grants are you submitting per year? Dr. Bean: About 20. Interviewer: This sounds totally insane—do you need so much money? What is your success rate? Dr. Bean: Same as everybody else's. About a 5% success rate; we send in 20 and we would be lucky if we get one; and I need at least 3 active grants to cover my lab's expenses. Interviewer: And what do you do with the grants that are not funded? Dr. Bean: Same as everybody else. We throw them in a blue plastic bin, otherwise known as the recycle bin. Interviewer: That's a shame, Dr. Bean. You just said that you spend your best time doing this and you now say that your best work ends up in the garbage? Dr. Bean: Yes, this is what I say. Interviewer: How much are you making per hour? Dr. Bean: About $100, and for 4 hours per day doing this, it is worth $400 per day, or roughly, $100,000 per annum. Interviewer: Going to garbage? Dr. Bean: Well, sort of. Interviewer: And how many Dr. Beans are doing this in the USA alone? Dr. Bean: Well, I do not know exactly but Google says that about 100 000 grant applications are submitted in the USA alone every year, so there must be at least 100 000 applicants like me! Interviewer: And if I multiply 100 000 by 100 000 do I get 10 billion dollars in wasted wages every year? Dr. Bean: Damn right! And you have not calculated the time wasted for panel members to review the grants, the administrative costs, etc.; the likely wasted wages may well be 20 billion instead or about 70% of the whole NIH budget! Interviewer: You seem to be very expensive and very wasteful, Dr. Bean; do the NIH officials know that? Dr. Bean: I do not know, ask them! Interviewer: And what do you advise the young scientists? Dr. Bean: I do not know—perhaps not printing their grants? Dispose of them electronically? But … wait a second here; I see that a new e-mail from NIH just arrived, and it reads: “we are pleased to inform you …” WHOA! My latest grant has been approved! I am sorry but I have to leave immediately to go and buy the champagne for the celebration with my lab and then leave for a 1-week vacation. I fully deserve it. I made it to the 5% of lucky applicants. See you in 1 week. I am coming back fresh to work on my next grant application.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,035
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,992
Score d'incertitude au seuil0,144

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,035
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0090,003
Communication savante0,0070,006
Science ouverte0,0010,003
Intégrité de la recherche0,0050,013
Charge utile insuffisante (le modèle a refusé de juger)0,0430,017

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,393
Tête enseignante GPT0,502
Écart entre enseignants0,109 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineÉvaluation
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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