STEVE MARKS, Module 8: Becoming a Trusted Digital Repository
Bibliographic record
Abstract
Steve Marks has accomplished something that very few people in the world have: he created a Trusted Digital Repository (TDR) that met the criteria of the Trustworthy Repositories Audit & Certification (TRAC).It was the first repository in Canada, and one of only six in the world.Because of the significance of this task, this publication is important to consider: the author went beyond theorizing how a TDR could be created and actually achieved it.Marks undertook this task when he was the digital preservation librarian at the Toronto-based Scholars Portal, a service of the Ontario Council of University Libraries (OCUL): the Scholars Portal e-journals database TDR passed the very stringent Centre for Research Libraries (CRL) audit and obtained the rare certification in February 2013. 1 To pass the audit and be granted certification, a TDR must demonstrate compliance with the TRAC criteria and the strict
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".