Make Stories That Matter: Innovative Techniques for Teaching Ethical and Inclusive Media-Making
Bibliographic record
Abstract
The focus of this research is to explore how a co-creation process and a constructive journalism philosophy can be applied to creating a filmmaking course. By identifying five main principles for effective constructive co-creation and embedding them into a digital education platform, a new educational approach has been created to empower and engage youth in creating stories that matter. Constructive Journalism requires a media-maker to address larger societal, environmental or systemic problems in their story while also showcasing possible solutions, innovations or progress made. This course is designed to allow students to choose a topic they are passionate about and partner with an inspiring individual who is leading change in that area, together they co-create a film using the constructive approach. The student's learning is broken up into five modules where each module begins with a fable that allows them to better understand key topics. Students progress at their own pace through a series of interactive videos which include self-tests, opportunities for reflection, and group online chats to socialize ideas. By the end of the course, students create their own "director's cut" with behind-the-scenes reflections and photographs to show their journey as well as a short film to be featured as part of Worldviews Productions digital platform. The course is designed to be scalable and versatile in nature so it can be offered digitally or in person.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".