Bibliographic record
Abstract
Inclusive schooling is now widely regarded as the most effective way to maximize the potential of all the students served by our schools. It has traditionally been associated with bringing disabled students into the regular classrooms but I use the term in a much broader way to embrace taking account of differences of all kinds – age, disability, race, culture, sex, sexual orientation, national origin, and other defining characteristics. An inclusive approach to schooling is a matter of increasing importance in a Canada that is more diverse and multicultural every day. Most educators now support inclusion as a theory but there are still significant debates about how best to implement the policies of inclusion.\nI am thus confident of wide support for the policy of inclusion. I am less confident that either educators or the general public will embrace the law, and in particular the concept of equality found in the Canadian Charter of rights and Freedoms and human rights codes, as the light- house that can guide educators down the path to inclusive schools. lawyers and judges are more often regarded as sources of fog shrouding the educational process than as beacons of light to guide educators through the complex fog of public education. nonetheless, I will argue that the concept of equality, properly understood and applied with adequate resources, can be the lighthouse that guides us to more inclusive, effective, and even safer public schools.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".