Why won't the pieces fit: Uncovering Deviations in the Compensation Awarded to Japanese Canadians at the Bird Commission
Bibliographic record
Abstract
In developing the Bird Commission, its commissioner, Henry I. Bird, and other officials eventually chose to compensate Japanese Canadians for the forced sale of their property below free market value using property categories and set percentages. They developed strict formulas for each category that should have been easy to follow when reimbursing claimants. However, this was not the case. Commission officials failed to follow the procedures that they had developed when awarding compensation to Japanese Canadians. Claimants could collect awards that were below or above the amounts that the commission’s procedure predicted. This thesis aims to understand the reasons why Bird Commission officials failed to follow the formulas that they had developed when compensating Japanese Canadians for the dispossession of their property through an examination of the Bird Commission Casefiles and Custodian Casefiles. Using information gathered from these government records, this analysis employs statistical analysis to explain the factors which influenced commission officials to alter awards. Considering the historical context of the commission, this analysis also offers explanations for why the factors uncovered using regression analysis may have impacted the commission and its outcomes. Recognizing the deviations in the Bird Commission’s compensation offers new insights into the commission’s operations and impacts on Japanese Canadians. It highlights a close relationship between the commission and officials from the Office of the Custodian of Enemy Property, and it participates with the work of other scholars in acknowledging the efforts that Japanese Canadians made in making the Canadian government confront the injustices it had conducted against them.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".