Bibliographic record
Abstract
When professors and students utilize the Internet, course web pages, and other online learning tools, much of the material that they make use of is protected by copyright law. A blend of case law and legislation governs the use of online materials and how technology can be used in the classroom and in school-related activities, often creating confusion for content users. Educators and students alike need to be familiar with applicable laws and need to understand the implications of common activities such as using technology in the classroom, conducting research on the Internet, and using multi-media in classroom presentations and student projects. Canadian copyright law is currently undergoing a much-needed process of reform, in order to bring Canadian copyright law in line with the laws of the rest of the world, and to keep up with rapidly changing technology that has changed the way people use copyrighted materials. This paper will focus on the legalities surrounding the use of technology and digital media in educational settings. It will provide guidelines for Canadian educators to ensure that their use of information technology and digital materials in the classroom is both appropriate and acceptable, and will propose strategies to manage those risks.
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 | 0.033 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.032 | 0.026 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".