Ontario High School Science Word List (OHSWL)
Bibliographic record
Abstract
This research aims to explain the development of an Ontario High School Science Corpus and subsequently an Ontario High School Science Word List (OHSWL). The OHSWL is a list of the most frequent technical words in the Ontario high school science curriculum. The science corpus was compiled from Ontario science textbooks and public written lecture material. A total of 803 lemmas were identified as part of the OHSWL. The coverage of the OHSWL in the science corpus vs non-science corpus is 7.79% and 1.52% respectively. The high frequency vocabulary (top 3,000 words) of the Corpus of Contemporary American English (COCA) and OHSWL had a coverage of 85.44% and 75.67% in the science corpus compared to the non-science corpus. With an approximately 10% difference in coverage, the OHSWL proves to be a significant source of vocabulary for an Ontario science learner. While coverage of the first and second 1,000 words of the COCA were greater in the science corpus compared to the OHSWL, coverage of the third 1,000 words was only marginally greater. Therefore, past the top 3,000 words of the COCA, the greatest value for someone learning the Ontario science curriculum is achieved by knowing the OHSWL. This corpus-based study has the potential of helping students in Ontario, regardless of whether they speak English as their first language or not.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 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; both teacher heads agree on what is shown here.
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".