Chronicling the Journey of the Society for the Advancement in Biology Education Research (SABER) in its Effort to Become Antiracist: From Acknowledgement to Action
Bibliographic record
Abstract
The tragic murder of Mr. George Floyd brought to the head long-standing issues of racial justice and equity in the United States and beyond. This prompted many institutions of higher education, including professional organizations and societies, to engage in long-overdue conversations about the role of scientific institutions in perpetuating racism. Similar to many professional societies and organizations, the Society for the Advancement of Biology Education Research (SABER), a leading international professional organization for discipline-based biology education researchers, has long struggled with a lack of representation of People of Color (POC) at all levels within the organization. The events surrounding Mr. Floyd’s death prompted the members of SABER to engage in conversations to promote self-reflection and discussion on how the society could become more antiracist and inclusive. These, in turn, resulted in several initiatives that led to concrete actions to support POC, increase their representation, and amplify their voices within SABER. These initiatives included: a self-study of SABER to determine challenges and identify ways to address them, a year-long seminar series focused on issues of social justice and inclusion, a special interest group to provide networking opportunities for POC and to center their voices, and an increase in the diversity of keynote speakers and seminar topics at SABER conferences. In this article, we chronicle the journey of SABER in its efforts to become more inclusive and antiracist. We are interested in increasing POC representation within our community and seek to bring our resources and scholarship to reimagine professional societies as catalyst agents towards an equitable antiracist experience. Specifically, we describe the 12 concrete actions that SABER enacted over a period of a year and the results from these actions so far. In addition, we discuss remaining challenges and future steps to continue to build a more welcoming, inclusive, and equitable space for all biology education researchers, especially our POC members. Ultimately, we hope that the steps undertaken by SABER will enable many more professional societies to embark on their reflection journeys to further broaden scientific communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.072 | 0.077 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.003 | 0.048 |
| Research integrity | 0.018 | 0.047 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".