Tailoring DEIA Programming through Current Field Analysis: Promoting Allyship in STEM of University Graduate Students
Bibliographic record
Abstract
Abstract Promoting diversity, equity, and inclusion (DEI) initiatives by most higher education institutions across the United States has been shown to cultivate environments more welcoming to students from historically marginalized backgrounds or underrepresented groups [1-2]. Within the literature, strategies to increase allyship within Science, Technology, Engineering, and Mathematics (STEM) are dominated by studies pertaining to male and female identities [3-7]; however, there are many demographics that are underrepresented within STEM that need to be addressed through targeted educational initiatives: these include, but are not limited to, age, sexuality, marital status, physical and cognitive disabilities, parental status – to include pregnancy, religion, nationality, race, ethnicity, and citizenship status [8-15]. To implement an effective program for graduate students aimed to commence and expand skills to be a lifelong ally for minorities in STEM, training needs to be intersectional in its development and execution need to be accessible and applicable to the current academic and industrial landscape [16-19]. Our team of STEM graduate students has been continually developing and improving our program aimed to promote allyship through annual workshops since its formation in 2019. Determining workshop programing has matured from choosing topics of interest of members planning program events, to researching the allyship needs throughout the university community and beyond. Our methodology for determining the current climate STEM graduate students from our institution are facing is through a survey designed to capture personal experiences with inclusivity and discrimination. The survey is distributed to not only students and faculty from our institution, but to other higher learning institutions, those employed in the STEM field within industry, and those who do not identify as being within the STEM community. The aim is to identify the atmosphere STEM graduate students currently face and what environment they will potentially enter upon completion of their degrees. Furthermore, our team will identify areas of discrimination that have the greatest impact on individual feelings of inclusivity and STEM retention curtailment. This paper will describe the process of our team developing our program's events since its inception and our findings on which areas of allyship are most critical today. [1] T. Cumming, M. D. Miller, and I. Leshchinskaya, "DEI Institutionalization: Measuring Diversity, Equity, and Inclusion in Postsecondary Education," Change: The Magazine of Higher Learning, vol. 55, no. 1, pp. 31–38, Jan. 2023, doi: 10.1080/00091383.2023.2151802. [2] M. Ryu, R. Bano, and Q. Wu, "Where Does CER Stand on Diversity, Equity, and Inclusion? Insights from a Literature Review," J. Chem. Educ., vol. 98, no. 12, pp. 3621–3632, Dec. 2021, doi: 10.1021/acs.jchemed.1c00613. [3] P. Arredondo, M. L. Miville, C. M. Capodilupo, and T. Vera, Women and the Challenge of STEM Professions: Thriving in a Chilly Climate. in International and Cultural Psychology. Cham: Springer International Publishing, 2022. doi: 10.1007/978-3-030-62201-5. [4] M. Flottat, "Authentic Allies: Fostering Male Allyship Within Stem," MA Leadership, Royal Roads University, Victoria, British Columbia, Canada, 2022. [5] M. Krentz, O. Wierzba, K. Abouzahr, J. Garcia-Alonso, and F. B. Taplett. "Five ways men can improve gender diversity at work." Boston, MA: Boston Consulting Group, 2017. [Online]. Available: https://web-assets.bcg.com/img-src/BCG-Five-Ways-Men-Can-Improve-Gender-Diversity-at-Work-Oct-2017_tcm9-173041.pdf [6] M. D. Powless et al., "'[He] gave me a 30-minute lecture on how I was a shitty grad student': Examining how male faculty can eradicate systemic oppression and support women in STEM.," Journal of Diversity in Higher Education, Feb. 2022, doi: 10.1037/dhe0000393. [7] M. A. Warren, S. D. Bordoloi, and M. T. Warren, "Good for the goose and good for the gander: Examining positive psychological benefits of male allyship for men and women.," Psychology of Men & Masculinities, vol. 22, no. 4, pp. 723–731, Oct. 2021, doi: 10.1037/men0000355. [8] D. C. Beardmore, "A Call to Make Queer Erasure, Violence, and Battle Fatigue in STEM Visible," in Queering STEM Culture in US Higher Education, 1st ed.New York: Routledge, 2022, pp. 57–72. doi: 10.4324/9781003169253-5. [9] E. Bell, K. Seymore, S. Breen, and M. McCullough, "Empowering Black Scientists in STEM: Early Success of the Black Biomechanists Association," Biomed Eng Education, vol. 2, no. 2, pp. 113–121, Sep. 2022, doi: 10.1007/s43683-022-00078-z. [10] R. Campbell-Montalvo et al., "Scientific Societies Integrating Gender and Ethnoracial Diversity Efforts: A First Meeting Report from Amplifying the Alliance to Catalyze Change for Equity in STEM Success (ACCESS+)," J Microbiol Biol Educ., vol. 23, no. 1, pp. e00340-21, Apr. 2022, doi: 10.1128/jmbe.00340-21. [11] M. S. Dudu, "Impact of Targeted Diversity, Equity, and Inclusion (DEI) Initiatives on the Retention and Graduation Rates of Students of Color at Community Colleges," DMPS, Hamline University, Saint Paul, Minnesota, 2023. [Online]. Available: https://digitalcommons.hamline.edu/hsb_all/26 [12] P. Dwyer, E. Mineo, K. Mifsud, C. Lindholm, A. Gurba, and T. C. Waisman, "Building Neurodiversity-Inclusive Postsecondary Campuses: Recommendations for Leaders in Higher Education," Autism in Adulthood, p. aut.2021.0042, Sep. 2022, doi: 10.1089/aut.2021.0042. [13] N. Kazmi, "Diversity, Equity and Inclusion within STEM in Canada: A Literature Review." University of Victoria, 2022. [Online]. Available: https://www.uvic.ca/coop/_assets/docs/partnerships-dei-lit-review.pdf [14] A. Ricci, F. Crivellaro, and D. Bolzani, "Perceived Employability of Highly Skilled Migrant Women in STEM: Insights from Labor Market Intermediaries' Professionals," Administrative Sciences, vol. 11, no. 1, p. 7, Jan. 2021, doi: 10.3390/admsci11010007. [15] J. Roberts and H. Shinn, "Diversity in STEM - Bibliography," 2018, doi: 10.25923/BK7R-3M38. [16] C. Miller Dyce and A. Owusu-Ansah, "Yes, We Are Still Talking About Diversity: Diversity Education as a Catalyst for Transformative, Culturally Relevant, and Reflective Preservice Teacher Practices," Journal of Transformative Education, vol. 14, no. 4, pp. 327–354, Oct. 2016, doi: 10.1177/1541344616650750. [17] R. Pinkett, Data-driven DEI: the tools and metrics you need to measure, analyze, and improve diversity, equity, and inclusion, First edition. Hoboken, NJ: Wiley, 2023. [18] R. Ramiah, L. Godinho, and C. Wilson, "Tertiary STEM for All: Enabling Student Success Through Teaching for Equity, Diversity and Inclusion in STEM," IJISME, vol. 30, no. 3, Aug. 2022, doi: 10.30722/IJISME.30.03.003. [19] T. E. Watson, "An Exploration of Women of Color Navigating Intersectionality in STEM Programs," Doctor of Education in Higher Education Leadership, Trident University, Chandler, Arizona, 2022. [Online]. Available: https://www.proquest.com/openview/2f3ddba01a6af2a75be7f0b618badaad/1?pq-origsite=gscholar&cbl=18750&diss=y
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".