Collecting Digital Object Reuse Data and Assessing it with Care
Bibliographic record
Abstract
The DLF AIG Content Reuse Working Group is developing tools and documentation to better assess the reuse of digital objects. This is particularly of relevance to GLAMR practitioners since there are no established definitions, standards, or common practices for the assessment of digital object reuse. This presentation will focus on two deliverables: a set of reuse assessment data collection Recommended Practices and Ethical Guidelines for assessing reuse. Presenters will provide an overview of the Recommended Practices and associated tools that practitioners can utilize to collect reuse assessment data. They will introduce the Ethical Guidelines and discuss how practitioners can use them to develop more considerate assessment practices for understanding the reuse of the collections they steward. The Guidelines aim to examine the social and political overtures of those working in and impacted by GLAMR environments, and the importance of aligning ethics, values, and accountability in the work process from which future peers draw. Presenters will conclude the talk by articulating how the Recommended Practices and Ethical Guidelines begin to address the lack of standards and common practices for assessing reuse as well as by situating these outputs in the IMLS-funded project "Digital Content Reuse Assessment Framework Toolkit."
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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 | 0.174 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".