The Admissibility of Business Records as Legal Evidence: A Review of the Business Records Exception to the Hearsay Rule in Canada
Bibliographic record
Abstract
Les spécialistes en gestion de l'information jouent un rôle important afin d'augmenter la probabilité que les documents d'archives des entreprises soient admis comme preuves.Cet article discute du cas des documents d'archives des entreprises comme exception à la règle et il examine les façons dont ils peuvent être mis en adjudication au Canada.En faisant l'étude de la jurisprudence canadienne, l'auteur examine plusieurs critères qui pourraient contribuer à la décision d'un juge d'admettre comme preuve un document d'archives d'entreprise et il identifie les façons dont les spécialistes en gestion de l'information peuvent venir en aide à leur organisation en satisfaisant à chacun de ces critères.L'auteur affirme qu'une gestion des documents solide et structurée permet à une institution d'augmenter la probabilité que ses documents d'archives d'entreprise soient admis comme preuve.ABSTRACT Records professionals play an important role in increasing the likelihood that business records will be admitted as evidence.This article discusses the business records exception to the hearsay rule and the ways in which business records may be tendered as evidence in Canada.By reviewing Canadian case law, the author examines several criteria that could contribute to a judge's decision to admit a business record as evidence and identifies ways that records professionals can help their organization satisfy each of the criteria.It is argued that the act of sound and structured recordkeeping helps an organization increase the likelihood that its business records will be admitted as evidence.This article is an adaptation of the author's doctoral dissertation, "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" (PhD diss., University of British Columbia, 203).
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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 | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.028 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".