Pursuing the “usual and ordinary course of business” : an exploratory study of the role of recordkeeping standards in the use of records as evidence in Canada
Bibliographic record
Abstract
Recordkeeping standards are intended to offer guidance and recommendations for the requirements of a sound and effective records management program. One of the uses of recordkeeping standards may be to help organizations ensure the trustworthiness of records in the event that an organization must present records for consideration as evidence in court. No empirical study has previously been conducted, however, to determine if standards provide suitable guidance about the relationship between recordkeeping principles and legal compliance. Therefore, no assessment has yet been made of whether standards contain sufficient or appropriate guidance to help organizations meet their obligations for the creation and management of legally admissible records. This dissertation addresses the nexus between recordkeeping standards and the admissibility of business records as evidence. The study uses case law from British Columbia and Ontario as a basis for comparing admissibility criteria in a selection of relevant cases against the content of a selection of relevant recordkeeping standards. The study aims to determine if the standards examined provide adequate recordkeeping guidance to help an organization support legal compliance with the criteria for admissibility identified. As an exploratory study, this research also presents an innovative model for considering the suitability and applicability of recordkeeping standards, particularly in relation to the legal obligations of an organization for records creation and management. The findings of this research show that the act of sound and structured recordkeeping helps an organization increase the likelihood that its business records will be admitted as evidence, but that standards play a very small part in actual decisions about admissibility. Case law from British Columbia and Ontario reveals that, in order to convince a judge that a business record is trustworthy, counsel needs to be able to demonstrate the existence of, and consistent application of, accurate and up-to-date recordkeeping documentation. The study offers recommendations on how recordkeeping standards could be strengthened so that they provide more robust and effective guidance about recordkeeping practices and the creation of records-related documentation, most notably in relation to legal issues associated with the admissibility of evidence.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".