From paper to cyberspace : changing communication technologies and the implications for personal records archivists
Bibliographic record
Abstract
an important part of the mandate of Canadian public ABSTRACT archives.Researchers and the wider society whích funds these archives will thus rightly expect that archivists in these archives will address the matter of personal electronic records.Chapter Two provides an overview of archival responses to institutional electronic records and archives.This will show that heavy emphasis has been placed on public or government electronic records.While this orientation can provide useful insights for personal records archivists, it does not address all of their concerns.Chapter Three will examine some of the most recent developments in computerized communications technologies people are using when creating personal documents.These technologies pose new challenges for personal records archivists.A short history of personal communications will put this challenge into context.Chapter Four will convey various archival responses to personal electronic records.Some archival institutions (including the Provincial Archives of Manitoba, the University of Manitoba Department of Archives and Special Collections, and the National Archives of Canada) have been surveyed to see how they have responded to personal electronic records and how they plan to do so in future.This thesis aims to raise awareness among archivists and researchers of the implications of personal electronic records for archives.This is an important, yet largely understudied, aspect of archives.lt is hoped that this study will further examination of this key problem by archivists and others alike.I would like to thank the University of Manitoba for offering the Archival Studies Program.This unique area of study provides students with an historical background, archival theory and practical skill unlike any other program in North America.Special thanks also go out to CarolAdam, University of Manitoba's History Graduate Secretary, for her friendliness, support and administrative assistance throughout my years at the University of Manitoba.Tom Mitchell first interested me in a career in archives and he deserves special thanks for both that and his involvement in this project.Adrian Cunningham's seminal article on this topic was the basis for this study, and his interest in my project was both helpful and insightful.Rick Barry also took great interest and time in aiding me throughout my study.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.022 | 0.036 |
| Scholarly communication | 0.052 | 0.034 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".