Digitising archival records : Benefits and challenges for a large professional accounting association
Bibliographic record
Abstract
With antecedents extending to 1886, CPA Australia is one of the world’s most significant professional accounting associations. Reflective of its long history and widespread influence, the organization holds an extensive and diverse archive that evidences both its own development and the general evolution of accounting and business practices. This article presents a case study of a project to digitize selected aspects of this archive. Informed by perspectives on managing archives in the digital era, the benefits and challenges of digitization are presented. A key benefit was enabling access to digital images while preserving rare and fragile original records and documents. However, challenges arose in prioritizing the items for digitization, and this necessitated the development of a model, taking the form of a decision matrix. The CPA Australia case study will be informative for other organizations seeking to use digitization as a means to overcome the dilemma associated with providing access to archival materials while also ensuring their preservation. © 2016, Association of Canadian Archivists. All rights reserved.
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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 | 0.001 | 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".