<sub>Valuing Historical Claims of Loss of Use of Land with Sparse Data</sub>
Bibliographic record
Abstract
Abstract There have been numerous historical claims by First Nations across Canada for damages resulting from the taking of land and the resulting loss of use of such land. Many of these cases have come before the courts. Generally in such cases, there is agreement that either the Federal Government and/or a provincial government has not fulfilled its fiduciary duty. Hence, the disputes before the courts usually pertain to valuing the losses of the First Nation(s) who is (are) the Plaintiff(s) in these cases. Since the original taking of the lands occurred many decades in the past, the court is challenged with difficult valuation issues, which are complicated by a lack of historical data and transaction records. Hence, even if the parties agree on the methodology for valuing the losses and on an annual lease rate, they still need to determine the annual price of the land. A common practice for generating a price trajectory for the land is to use a very small sample of land prices, and interpolate between these prices to estimate the intervening land prices. This practice does not generate the expected trajectory given the known observations. It implicitly assumes a deterministic price process with an annual fixed appreciation of the asset throughout the period. These assumptions are inconsistent both with realistic price movements and the literature modeling asset price processes. Consequently this practice can, and mostly does, generate a very significant bias in the value of the loss. This paper suggests a loss of use valuation method that is based on a land price process consistent with the literature modeling asset price processes.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".