37 Matching Annotations
  1. Feb 2021
    1. A final suggestion for STEM profes-sionals is to use clear and objective criteria for evaluatingSTEM job applicants.

      While I like the intent, I think this would be very hard to implement because it is tough to come up with an exact formula for candidates' rating for a given job.

    2. One way to do this might be to have STEMjob candidates submit materials that do not include their fullnames, but only surnames, which are inevitably present incitations of publications and presentations. This may reducethe operation of gender biases in the evaluation of candidates’materials, although racial biases may still emerge as the resultof racially or ethnically linked surnames.

      We should recommend this to our faculty recruiting committee

    3. Contrary to Hypothesis 3, there were no significant interac-tions between race and gender on perceived competence

      interesting negative result...

    4. Thus, consistent with Hypothesis 4, our results indi-cated that only physics faculty appeared to exhibit gender biasfavoring male candidates in terms of both perceived compe-tence and hireability

      yay for biology, but again, Scripps' lack of diversity at the faculty level is not a good sign...

    5. Pretesting

      This section really describes the rigor with which they created these fictional candidates. Always wondered how they did this...

    6. Based on the stereotype content model (Fiske et al. 2002),as well as previous research examining faculty gender biasesin STEM (Moss-Racusin et al. 2012), we predict that malepost-doctoral candidates, overall, will be rated as higher incompetence and hireability than female post-doctoral candi-dates across physics and biology departments (Hypothesis 1).We also predicted that White and Asian candidates would berated as more competent and hireable than Black and Latinxcandidates across departments (Hypothesis 2). Furthermore,consistent with intersectionality theory and prior research, wepredict that the White and Asian male candidates, comingfrom multiple social backgrounds associated with success inSTEM, would be seen as the most competent and hireable ofall race-by-gender targets, whereas women candidates fromBlack or Latina backgrounds, who face multiple descriptiveexpectations to be low in STEM aptitude, would be rated theleast competent and hireable of all race-by-gender targets(Hypothesis 3). Because some previous research suggests thathighly male-dominated fields are associated with greater gen-der bias and inequity (Cheryan et al. 2017;Riegle-CrumbandKing 2010), we also predict that the gender biases we observewould be attenuated by department, with faculty from biologydepartments showing a weaker preference for male post-doctoral candidates than faculty from physics departments(Hypothesis 4).

      solid set of hypotheses

    1. these organizations recommended that standardized tests called the graduate record examinations be eliminated as a requirement for admission

      Scripps eliminated the GRE requirement.

    2. This department used a more targeted approach to hire female faculty members who were strategically aligned with its intellectual interests. A star recruit led to more women coming aboard, which led to an increase in the enrolment of female chemistry graduate students.

      If diversity among faculty is a key driver for diversity among students, Scripps is ... not doing well...

    3. How


    1. Second, a strong sense of community lies at the heart of the program, because people persist and thrive when they feel as though they belong to something bigger than themselves. Students enter as a cohort of about 60 students. They live together on campus. We strongly encourage group work, so that students learn from one another and thrive together. We speak of the “Meyerhoff family.”

      This is a challenge for our small graduate program since our entire grad student cohort is ~60 each year. Getting critical mass to build a feeling of community is tough.

    2. How


    1. A number of companies have gotten consistently positive results with tactics that don’t focus on control. They apply three basic principles: engage managers in solving the problem, expose them to people from different groups, and encourage social accountability for change.

      great insights below -- hard to single any one section/sentence out. I definitely will be taking this one back to the Scripps DEI committee...

    2. Trainers tell us that people often respond to compulsory courses with anger and resistance—and many participants actually report more animosity toward other groups afterward. But voluntary training evokes the opposite response (“I chose to show up, so I must be pro-diversity”), leading to better results

      I like this model -- voluntary training that can be taken at any time. Perhaps pair it with public disclosure of who's taken the training as the tiniest hint of peer pressure...

    3. Do people who undergo training usually shed their biases? Researchers have been examining that question since before World War II, in nearly a thousand studies. It turns out that while people are easily taught to respond correctly to a questionnaire about bias, they soon forget the right answers. The positive effects of diversity training rarely last beyond a day or two, and a number of studies suggest that it can activate bias or spark a backlash.

      On quick skim, it seems like these sentences offer a pretty simplistic summary of the linked paper. Not surprisingly, it seems like there is a lot of nuance in the types of diversity training that one can do. (Having said that, I think formal assessments of various DEI-related interventions is a very good idea.)

    4. Why


  2. Mar 2017
  3. Sep 2016
    1. or body. Inside the jack, the contacts are suspended diagonally toward the insertion interface. W

      test annotation

    2. hen holding

      test annotation2

  4. Apr 2016
    1. trastuzumab

      Wikidata:Q412616 CHEMBL:CHEMBL1201585

    2. apoptosis

      Wikidata:Q14599311 GO:GO:0006915

    3. ErbB2

      Wikidata:Q415271 Uniprot:P04626

    4. estrogen receptor-negative (ER-negative) breast cancer

      Wikdata:Q18553637 DOID:0060076

  5. Jul 2015
    1. a number of text-mining tools aimed at supporting biomedical text extraction, fact finding and text summarization. Some of the better-known or more widely used tools include EBIMed (4), CiteXplore (5) and GoPubMed (6)

      would be good to check these out

  6. Sep 2013