Bibliographic record
Abstract
While many may think of it as an "invention" of the modern age, intellectual property ("IP") has existed since at least as early as the 17th Century with the advent of the Statute of Monopolies in the U.K. Intellectual property has evolved significantly since then into an important aspect of modern day society touching all of our lives in some form or another Canadian health care in the 21st Century is no exception. This article attempts to provide health care professionals who may not be familiar with this subject matter with a general overview of what is "intellectual property". Many readers may be aware ofintellectual property on some level but may not understand how the various types of IP function and interrelate, as well as the possible impact on the nature and scope of health care services. The purpose of this article is to attempt to provide the reader with the tools, definition and 'jargon" to understand IP so that they can appreciate the issues discussed in greater detail in the remaining papers of this special edition.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.052 | 0.048 |
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".