Teachers and the Law: Diverse Roles and New Challenges, 5th Edition
Bibliographic record
Abstract
An invaluable resource for teachers and other education professionals, Teachers and the Law provides the legal knowledge necessary to fulfill multiple roles and to succeed in the modern Canadian classroom. Teachers will be equipped to address a wide range of issues, including technology and social media, harassment, bullying, censorship, Indigenous education, privacy, equality, and more. This edition reflects updated legislation relating to the ever-evolving rights of teachers, students, and parents, as well as emphasizing the impact that courts and human rights tribunals have in shaping educational policies and practices. It also features updated coverage of both societal and national challenges facing today’s teachers, including the use of technology in classrooms, increases in school violence, the lingering effects of the COVID-19 pandemic, and more. Chapter 1: Introduction to the Legal Framework Chapter 2: Teachers as Parents Chapter 3: Teachers as Educational State Agents Chapter 4: Teachers as Guardians of Equality Chapter 5: Teachers as Agents of the Police Chapter 6: Teachers as Social Welfare Agents Chapter 7: Teachers as Employees Chapter 8: The Role of Technology in the Classroom and Beyond Chapter 9: The Modern Teacher: Adapting to Evolving Realities Appendix 9A: A Framework for Ethical Decision Making
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".