COPYRIGHT LITERACY AND OPEN LICENSE ATTRIBUTION AS SCHOLARLY PRACTICE
Bibliographic record
Abstract
Aspects of copyright literacy and attributing open licenses as scholarly practice inform this commentary. Because citation practices have a much longer history than attribution, an overview of the study of citation and its relationship to the developing practice of attributing open licenses provides a model and trajectory to follow. Copyright literacy as part of attribution knowledge and skills bifurcates from citation scholarship, yet it is part of reconsidering and affirming knowledge connections. Decolonizing perspectives of epistemology and what counts as knowledge, ownership, and sharing are part of this bifurcation that involves attribution, Indigenous ways of knowing, and Traditional Knowledge Labels. There are also tensions involved with properly attributing Creative Commons licenses and the title, author, source, and license process offers an imperfect and sometimes complicated pathway forward. Through this process, accurate and effective license acknowledgement occurs for newly created, reused, revised, remixed, or reshared artifacts. It is suggested to use the online attribution builder and best practices for attribution placement are provided for written documents, presentations, blog posts, videos, and other formats. As part of open education practices, attribution signals contributions to the knowledge commons and are part of copyright literacy and professional digital competence.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".