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Record W3215344109 · doi:10.3389/feduc.2021.780401

Chronicling the Journey of the Society for the Advancement in Biology Education Research (SABER) in its Effort to Become Antiracist: From Acknowledgement to Action

2021· article· en· W3215344109 on OpenAlexaff
Miriam Segura‐Totten, Bryan M. Dewsbury, Stanley M. Lo, Elizabeth G. Bailey, Laura Beaster‐Jones, Robert J. Bills, Sara E. Brownell, Natalia Caporale, Ryan D. P. Dunk, Sarah L. Eddy, Marcos E. García‐Ojeda, Stephanie M. Gardner, Linda E. Green, Laurel Hartley, Colin Harrison, Mays Imad, Alexis M. Janosik, Sophia Jeong, Tanya Josek, Pavan Kadandale, Jenny Knight, Melissa E. Ko, Sayali Kukday, Paula P. Lemons, Megan Litster, Barbara Lom, Patrice Ludwig, Kelly McDonald, Anne C.S. McIntosh, Sunshine Menezes, Erika M. Nadile, Shannon Newman, Stacy D. Ochoa, Oyenike O. Olabisi, Melinda T. Owens, Rebecca Price, Joshua W. Reid, Nancy Ruggeri, Christelle Sabatier, Jaime L. Sabel, Brian K. Sato, Beverly L. Smith‐Keiling, Sumitra Tatapudy, Elli J. Theobald, Brie Tripp, Madhura Pradhan, Madhvi J. Venkatesh, Mike Wilton, Abdi M. Warfa, Brittney N. Wyatt, Samiksha A. Raut

Bibliographic record

VenueFrontiers in Education · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcknowledgementScholarshipRacismSociologyPublic relationsDiversity (politics)Representation (politics)Inclusion (mineral)Professional associationEconomic JusticePolitical scienceSocial scienceLawGender studiesAnthropology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.417
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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