Bibliographic record
Abstract
Part I of this book looks at the principles and theories within which archival practice is situated. How do we define archives, and what types of material fall within and outside that definition? How did archival theory and practice develop throughout history, and how do we position our work today within existing theoretical frameworks? Theory notwithstanding, how do people actually use archives? What types of institutions are created to hold archival materials, and what are the similarities or differences between them? Regardless of institution, what are the guiding principles – the golden rule(s) – of effective and ethical archival service? And, especially in a world abounding with cloud computing systems, data security concerns, identity theft and 24-hour news cycles, how can the archivist balance the right of citizens to access evidence with the right of individuals to retain their privacy? These topics are addressed in the following chapters: Chapter 1: What are archives? Chapter 2: The nature of archives Chapter 3: Archival history and theory Chapter 4: The uses of archives Chapter 5: Types of archival institution Chapter 6: The principles of archival service Chapter 7: Balancing access and privacy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".