Nikola Tesla in science textbooks
Bibliographic record
Abstract
Nikola Tesla is considered one of the greatest inventors in modern history, being called “the man who invented the 20th century”. He was historically recognized by the naming of the magnetic field unit “tesla”, which is one of only 19 units named after scientists in the International System of Units (SI). Science textbooks mention a number of scientists when their work is related to a given topic and frequently include a biographical note with a photo. Tesla’s name is often said to have been forgotten in time. The lack of awareness of the significance of his work can be attributed to the apparent under representation of his accomplishments in educational resources. Conveying an accurate narrative of the evolution of scientific ideas developed by scientists should allow young generations to better understand the scientific process and motivate future scientists. In this work, we discuss the importance of the portrayal of historical figures in science textbooks, using Nikola Tesla as a case study among prominent scientists in textbooks spanning the Canadian education system (K-12 and university levels). The analysis reveals significant variations in representations based on textbook levels. There are also differences found among individual scientists, with some receiving significant coverage, while others appear to be under-represented. We hope that this project will motivate similar research, applying our methodologies to further the inclusion of other under-recognized scientists, for example, women or Indigenous scholars, and enhance the potential and benefits of more diverse science education.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".