Change the Numbers: Equity, Diversity, and Inclusivity in STEM | Imogen Coe | TEDxRyersonU
Bibliographic record
Abstract
In her talk, Imogen Coe addresses the lack of female representation in STEM; science, technology, engineering and math. Using humour and real life examples, she encourages women (and their allies) to break barriers, challenge stereotypes and demand their opportunity to contribute to science. Dr. Imogen Coe has worked extensively as an academic scientist and administrator, challenging stereotypes and breaking barriers for girls and women in her field. Imogen sees the the lack of equity, diversity and inclusivity in STEM at Ryerson University in her role as professor and dean of the faculty of science. Low confidence levels and under-representation of young women in STEM disciplines means that we are losing important skill sets and talent, limiting our ability to solve complex problems such as climate change. Imogen is an advocate for women in STEM and she uses data and evidence to demonstrate unfair practices, while providing suggestions on how to fix the system. As someone who was raised with a strong sense of social justice, Imogen believes in the fundamental right of everyone to contribute and participate in STEM to their full potential. Imogen firmly believes that EDI in STEM is not just a woman’s rights issue, but a human rights issue. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".