Educators as Content Creators in a Diverse Digital Media Landscape
Bibliographic record
Abstract
This paper focuses on the ways educators can work within the guidelines of copyright while using digital media to develop educational content in legal and ethical ways by exploring several key contemporary trends in digital content creation. Educators need not create learning materials from scratch, as the pool of resources available via the internet, from educational publishers, or physically stored within schools serve as resources for the development of contextually relevant learning materials. Educators are increasingly becoming content creators, and with the development of digital literacies to support content creation, they can combine resources from multiple sources to meet the needs of their learners. This may be done to ensure a lesson is current, meet the needs of differentiated instruction and universal design for learning, to design learning materials that are engaging, and those that can be shared widely. In this digital media landscape, it is important for educators to know how to navigate digital media for use in developing curriculum or learning materials. By drawing on the digital literacies associated with copyright and open educational practices, educators can work within the guidelines of fair use, link and embed content, recognize and use openly licensed content, and explore resources from the public domain in legal and technically appropriate ways while developing learning materials. These approaches may impact educators’ design processes, while also demonstrating and modelling to learners the creative ways one can remix and share resources found on the web.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".