Digital Authenticity and Integrity: Digital Cultural Heritage Documents as Research Resources
Bibliographic record
Abstract
This article presents the results of a survey addressing methods of securing digital content and ensuring the content's authenticity and integrity, as well as the perceived importance of authenticity and integrity. The survey was sent to 40 digital repositories in the United States and Canada between June 30 and July 19, 2003. Twenty-two institutions responded, the majority of which felt that ensuring authenticity and integrity represented a low priority compared to increasing access and preserving content. Technology for securing content and ensuring the authenticity and integrity of individual digital items has not yet been implemented at the majority of the responding institutions; however, the responses indicate that the number of institutions incorporating this type of technology will increase. The low level of concern and lack of implementation signify the need for additional research and interest in these issues, as the value of repository content for research purposes directly relates to the researcher's ability to trust the content's authenticity and integrity.
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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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".