Bibliographic record
Abstract
I was born in Rio de Janeiro, Brazil. In my hometown, around 1.5 million people (25% of the population) live in slums. I grew up experiencing house instability and food insecurity - a reality faced by tens of millions of Brazilians. Never in my wildest dreams did I imagine that one day I would become a scientist and professor. Against all odds, I did. Brazil is one of the most violent countries to be a women and the most violent country in the world for queer women. During the last five years, Brazil registered more than 15 murders of women every day.1Morais F.C.C. de Garcia Alves Feitosa V.M. Azevedo L.B. et al.Victims of sexual exploitation and violence in Brazil.J Pediatr Nurs. 2021; (Nov 24:S0882-5963(21)00351-1)https://doi.org/10.1016/j.pedn.2021.11.020Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar During the last 10 years, the country responded for over 40% of all murders of transgender and gender diverse persons in the world.2Transgender Europe (2022). Trans murder monitoring report. Available at: https://transrespect.org/en/map/trans-murder-monitoring/Google Scholar In both tragic scenarios, the heavier burden is among People of Color. My trajectory from poverty to academia was only possible because gender-based, affirmative policies were available. Because of those affirmative actions, I was able to enroll in and graduate from Ivy League universities. However, I was always an outlier. I was part of a minority surrounded by White peers and mostly White, male professors. On campus, security frequently stopped and demanded to see my badge, while my White peers could easily pass them - even without their own badges. At the university cafeteria, I was frequently stopped by students or faculty members complaining about an unclean bathroom or room. Those daily micro aggressions were common for Black and Latinx students - a clear demonstration that somehow we did not seem to belong. Systemic racism on campus is still very common. Black, Latinx Ivy League students are still underrepresented as of today. Programs adopted to correct historic discrimination against the Black and Latinx community from Harvard, Yale and Princeton, among others, faced extreme opposition from the Trump Administration.3Time. “The trump administration is set to probe college affirmative action for discriminating against white students”. August 3, 2017. Available at: https://time.com/4883793/justice-departmentcollege-admissions-affirmative-action/Google Scholar Studies have shown that Black and Latinx borrow student loans at higher rates than their White peers. In the US, white households have at least 10 times the median income of Black and Latinx households, making White students more likely to receive help from their families to pay for their education. As a Latinx immigrant, first generation college student living in the US, without affirmative actions I would not be able to pay for my studies. Research has shown that the fallout of the student debt crisis is disproportionately borne by individual student loan borrowers of color, especially Black and Latinx borrowers.4Student Borrower Protection Center. Disparate debits: how student loans drive racial inequality across American cities (2020). Available at: https://protectborrowers.org/wp-content/uploads/2020/06/SBPCDisparate-Debts.pdfGoogle Scholar This disadvantaged scenario add to a preexisting racial/ethnic wage gap and harm upward mobility. After my PhD graduation, I saw myself fighting (once again) systemic-level biases to continue in academia. According to the American Association of University Professors (AAUP),5American Association of University Professors. Data snapshot: full-time women faculty and faculty of color. (2020) Available at: https://www.aaup.org/news/data-snapshot-full-time-women-faculty-andfaculty-color#.Yfr_Ii2ZO3fGoogle Scholar women, and particularly women of color, continue to face deep disparities in academia. Women faculty, especially women of color are underrepresented in academia's highest faculty positions – and overrepresented in its more precarious ones. The higher the academic rank, the lower the percentage of women - for Black and Latinx women the percent is even lower. In the US, only 5.2% of full-time faculty members self-identify as Hispanic or Latinx and 6% identify as Black or African American, even though they're 17.5 and 12.7% of the country's population, respectively.6American Association of University Professors. Data Snapshot: Full-Time Women Faculty and Faculty of Color. (2020) Available at: https://www.aaup.org/news/data-snapshot-full-time-women-faculty-and-faculty-color#.YnqtyC2ZO-o.Google Scholar The AAUP's analysis confirms that women faculty members, particularly Black and Latinx, continue to face unique challenges in academia with respect to employment, career advancement, salary, and job security, and that higher education is by no means immune from systemic racism. Systemic racism and gender inequality in Academia is undeniable. Although some institutions have been more upfront to make necessary changes, there is still a long way to go. According to the Association of American Medical Colleges (AAMC), only 5.5% of medical school faculty are Hispanic, Latinx, or of Spanish origin; 3.6% are Black or African American; and 0.2% are Native American or Alaskan Native. We do not need more trainings to address implicit bias. We need reliable, accessible and responsive structures supporting accountability. We need prompt response to denounces of micro aggressions and discrimination. Academia needs to assure diversity in search committees, support for students and faculties from racial and/or gender minorities to continue in academia, to cite just a few key strategies. The Ivy towers need to continue working to improve their commitments and strategies towards an environment where justice, equity, diversity and inclusion are not empty promises, but the reality. Monica Malta. None.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".