Women in Science: 50 Fearless Pioneers Who Changed the World by R. Ignotofsky
Bibliographic record
Abstract
Ignotofsky, Rachel. Women in Science: 50 Fearless Pioneers Who Changed the World. 10 Speed Press, 2016.“It’s a Scientific Fact: Women rock!” This is the statement on the back cover of Rachel Ignotofsky’s fabulous book about women in science. This illustrated hardcover book surveys 50 women scientists’ achievements and biographies in bold style. The book includes women scientists ranging from agriculture, mathematics, chemistry, geology all the way to particle physics and astronomy. Each scientist has been allotted a two-page spread with a full-page biography, that is illustrated with bright and colourful drawings relevant to their discoveries and areas of research. Dispersed between the biographies are info-graphic sections that showcase scientific implements, a glossary, and even statistics about women in STEM.I was immediately drawn to this book by the colourful illustrations (also drawn by Ignotofsky) on both the cover and interlaced throughout the glossy pages of this book. The biographies strike an excellent balance between detail and brevity. I thoroughly enjoyed reading about the many women scientists I had never learned about before, like Hypatia, a mathematician who lived in Alexandria, Egypt in 350 CE, Emmy Noether who worked for Einstein’s team on the theory of relativity, Cecilia Payne-Gaposchkin who discovered the sun was comprised of Hydrogen and Helium and Rosalind Franklin who discovered the DNA double helix. This book left me with an overwhelming sense of the remarkable discoveries by women in science.Women in Science can be enjoyed all ages of readers, including adults. Older readers will enjoy the facts and information within the biographies, while younger readers can read the many illustrations. This book would be especially great to share with young girls, to inspire curiosity and interest in the sciences, and to show that they can follow in the footsteps of many great women scientists. Highly recommended.Highly recommended: 4 out of 4 stars Reviewer: Hanne PearceHanne Pearce has worked at the University of Alberta Libraries since 2004. She holds a BA and MLIS and is currently working towards her Master of Arts in Communications and Technology. Her research interests include: visual communication, digital literacy, information literacy and the intersections between communication work and information work. She is also a freelance photographer and graphic designer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".