Notice bibliographique
Résumé
Philanthropy contributes significantly to science and medicine. Even when adjusted for inflation, philanthropic support in the United States and Canada has steadily increased over the past decades. This level of interest and significant flow of research dollars is truly needed, and is so important that most centers and institutes today in many universities and hospitals carry the names of the patrons who have invested in these initiatives. Given this level of support, it is curious that state/provincial/federal governments and regulatory bodies know little about how this money is distributed and spent. Scientists themselves are usually unaware of their distribution practices. Critics worry that the philanthropic billions tend to support specific areas of research and are directed towards those in the traditional “scientific elite” -typically white and male - at the expense of diversity of ideas and people, especially women and particularly women of color (e.g. Black, Latina, etc.). Two prominent examples include the recent scandal at the MIT Media Lab with Jeffrey Epstein's millions at play [[1]Mervis J. What kind of researcher did sex offender Jeffrey Epstein like to fund? He told science before he died.Sci. Mag. 2019; (Available from:) (Accessed October 14, 2019)https://www.sciencemag.org/news/2019/09/what-kind-researcher-did-sex-offender-jeffrey-epstein-fund-he-told-science-he-diedGoogle Scholar], and the significant experiences of discrimination regarding private funds allocation described by senior female scientists at the Salk Institute [[2]Pickett M I want what my male colleague has, and that will cost a few million dollars.NY Times Mag. 2019; (Available from:) (Accessed October 14, 2019)https://www.nytimes.com/2019/04/18/magazine/salk-institute-discrimination-science.htmlGoogle Scholar]. These practices not only withhold private support for women, but can also undermine the desire of donors to promote the best science. Another common use of private funding is endowed professorships (also known as named chairs). Holding such endowed professorships is considered to be an honor in academia. Endowed chairs are overwhelmingly white and male. In Canada, the Canada Research Chairs (CRC) serve the same purpose [[3]Government of Canada. Canada Research Chairs. About Us. 2019. Available from:http://www.chairs-chaires.gc.ca/about_us-a_notre_sujet/index-eng.aspx [Accessed October 14, 2019].Google Scholar] and suffer the same fate regarding lack of diversity and inclusion. Only very recently the CRC expressed its strong commitment to equity, diversity and inclusion [[4]Government of Canada. Canada Research Chairs. Open Letter to University Presidents and Vice-Presidents from the Canada Research Chairs Program: 2019 Addendum to the 2006 Canadian Human Rights Settlement Agreements. 2019. Available from:http://www.chairs-chaires.gc.ca/program-programme/2019_open_letter-eng.aspx [Accessed October 14, 2019].Google Scholar]. At the core of this commitment is the profound understanding that achieving a more equitable, diverse and inclusive research cadre is essential to advancing science. Senior scientists like myself welcome this commitment. Those inspired to give can investigate the practices regarding gender equity and inclusion where they are investing. Men and women proudly sharing their name with scientists in endowed chairs can significantly foster gender and racial equity. Indeed, several institutions together with their donors are now taking steps to address this issue [[5]John Hopkins Institute for Clinical & Translational ResearchDoris Duke Early Clinician Investigator Award.2019https://ictr.johnshopkins.edu/funding_opps/funding-opportunities/doris-duke-early-clinician-investigator-award/Google Scholar]. Supporting excellent women in academia is a strategic decision not only for science, but also for a more equitable world.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».