Inspiring Diversity in STEM Conference 2022
Bibliographic record
Abstract
Inspiring Diversity in STEM is a grassroots initiative based in London, Ontario, Canada promoting diversity and inclusion in science, technology, engineering, and mathematics (STEM). Our mission is to help break down barriers that underrepresented groups face by connecting them with inspiring and representative role models, cultivating early research skills, and providing a supportive community. Together, we are building an inclusive community dedicated to helping future leaders achieve their goals. We hope to be a more inclusive community for people from various backgrounds, including race, ethnicity, gender and gender identity, age, socioeconomic status, national origin, sexual orientation, ability, and religion. Inspiring Diversity in STEM has run three successful biennial conferences thus far, in 2016, 2018, and 2022 and will be hosting its next conference in March 2024. Our conferences include keynote speakers, workshops, panel discussions, industry and graduate program expos, and an undergraduate poster competition. During each conference iteration, we award several students with poster presentations awards. We also provide travel awards to students travelling from outside of London, Ontario. Our conferences have brought in hundreds of attendees, ranging from the undergraduate to the faculty level, and several local industry partners. To learn more about us, please visit our organization's page.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.051 |
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".