Bibliographic record
Abstract
Abstract In this article, I argue that that the primary goal of statues honouring public figures is to create and shape a collective identity. The way that these statues further the goal of identity is not by holding up the subjects of the statues as admirable but rather by asserting that the subjects were in some way objectively important and central to some group surrounding the statue. I will look at the defences for keeping statues of and awards named after John A. Macdonald and show that the primary concern is not with defending the character of Macdonald but rather that removing him is ‘erasing history’. These defences are not about defending Macdonald as a person but rather defending a conception of the Canadian identity that requires Macdonald play a central role. Against these defences of Macdonald, I show that the ‘objective history’ case for him and other such similar figures fails. In the particular case of Macdonald, it fails because he was actually not that important for Canadian history. In the general case of negative public figures, I provide a short defence of how group identities are not static and not unchangeably rooted in a single historically based articulation.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".