The reliability of financial information of charitable organizations: an exploratory study based on the Benford’s Law
Bibliographic record
Abstract
Benford's Law (BL) is a logarithmic distribution which is useful to detect abnormal patterns of digits in number sets. It is often used as a primary data auditing method for detecting traces of errors, illegal practices or undesired occurrences, such as fraud and earning management. In this descriptive study, I analyzed the financial information (revenue and expenditure) of the registered charitable hospitals located in Ontario and Quebec, which have the majority (71.4%) of these organizations within Canada. The aim of this study was to verify the reliability of the financial data of the respective hospitals, using the probability distribution predicted by Benford’s Law as a proxy of reliability. The sample was composed by 1,334 observations related to 339 entities operating in the tax year 2009 and 328 entities in 2010, gathered from the Canada Revenue Agency’s database. To analyze the discrepancies between the actual and expected frequencies of the significant-digit, two statistics were calculated: Z-test and Pearson’s chi-square test. The results show that, with a confidence level of 95%, the data set of the organizations located in Ontario and Quebec have similar distribution to the BL, suggesting that, in a preliminary analysis, their financial data are free from bias.
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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 | 0.018 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".