Sir John A Macdonald and the Controversy of Statues Dedicated to Him
Bibliographic record
Abstract
This website offers an educational guide to help readers understand the historical events that Sir John A Macdonald set in motion that allowed for the oppression of the Indigenous peoples of Canada, and why the statues that are erected in his honour are receiving backlash in modern day, and how we could solve this issue. The website is divided into sections, with the homepage introducing the project and what our goal of the website is in educating viewers on historical events that are affecting us now. There is then a section dedicated to Macdonald, explaining the events and acts he was involved with that directly affected the Indigenous population. There is then a section on the statues that are currently facing backlash in the communities and need changes. We then have a viewpoint page that offers looks into both sides of the argument on the statues, as Macdonald is an important aspect of Canadian history as the first Prime Minister, and additionally why the statues are offensive and represent a racist man who set in motion cultural erasure. The last section is our resolution, where we provide a solution to this problem by suggesting additional plaques explaining the things he did while in office as well as erecting statues and commemorations celebrating Indigenous people and their culture.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".