Inclusive Science Communication Approaches Through an Equity, Diversity, Inclusion, and Social Justice (EDISJ) Lens
Bibliographic record
Abstract
Science communication has taken center stage in Science, Technology, Engineering, and Math (STEM) disciplines in the context of public outreach and citizen science. Developing practical communication skills is imperative for all scientists to be highly successful in their careers and more so for underrepresented and Black, Indigenous, and People of Color (BIPOC) professionals in STEM. The program, led by the Engineering and Science Librarian at the University of Victoria (UVic) Libraries, aimed to equip students and early career scientists with critical communication skills by leveraging the unique voices and lived experiences of BIPOC speakers in STEM disciplines. Through this program, a unique toolkit with engaging modules consisting of 30 short videos, each three minutes long (30 x 3) by BIPOC speakers was created to provide broad foundational skills in verbal and visual communication, using an Equity, Diversity, Inclusion, and Social Justice (EDISJ) lens. A two-day conference offered networking and communication development opportunities to students and early-career scientists in STEM disciplines by connecting them with BIPOC STEM leaders and visionaries who promote STEM advocacy. This paper will discuss the methods used in the creation of the toolkit and conference using an EDISJ lens.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".