arginal gains in accuracy of valuation from increasing the specificity of price indexes : empirical evidence for the Canadian economy
Bibliographic record
Abstract
In this paper we present empirical estimates of marginal gains in accuracy of asset valuation from increasing the specificity of price indexes used to adjust Historical Cost Financial Statements. The empirical evidence strongly suggests that the accuracy function for the Canadian economy is highly convex. This implies that the marginal gain in accuracy of valuation declines sharply as the number and specificity of price indexes used for valuation increases. These findings are potentially valuable for Auditors, Academics and Regulatory agencies who are involved in the debate on selection of asset valuation rules. The results are of particular relevance for selection of asset valuation rules when it is costly to use finer measurement methods. CICA handbook (Section 4510) presently allows companies to choose from several alternative methods for adjusting historical prices. In this paper we show the benefits obtained by using finer data, so that companies can made a decision as to the amount of resources they should invest to obtain finer data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".