Perspectives on Personal Archives
Bibliographic record
Abstract
In fact, we believe a computer functions much more like an environment -or a writing space, to use a term popularized by Jay David Bolter.Access to an entire computer is not unlike having a key to an author's study or workroom.Matthew G. Kirschenbaum et al., Approaches to Managing and Collecting Born-Digital Literary Materials for Scholarly Use, White Paper to the NEH Office of Digital Humanities, May 2009, 23This issue of Archivaria focuses on personal archives, those defined as the archives created by individuals and family groups.The guest editing of this volume was an initiative of the Special Interest Section on Personal Archives (SISPA) of the Association of Canadian Archivists, and the guest editors are all members of SISPA.When first considering the issue, the group sought to elicit papers with both scholarly and practical perspectives about approaches to personal archives from archivists and others who work with personal archives, as well as to elaborate future research and discursive possibilities for this field of interest.Until recently, archivists dealing with personal archives have had a modest number of scholarly resources upon which to draw.In particular, issue 24 (1996) of the
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 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".