THEORY AND METHODS Whiteboard animation for knowledge mobilization: a test case from the Slave River and Delta, Canada
Bibliographic record
Abstract
Objective. To present the co-creation of a whiteboard animation video, an enhanced e-storytelling technique for relaying traditional knowledge interview results as narratives. Design. We present a design for translating interview results into a script and accompanying series of figures, followed by technical steps to create a whiteboard animation product. Method. Our project used content analysis and researcher triangulation, followed by a collaborative process to develop an animated video to disseminate research findings. A 13-minute long whiteboard animation video was produced from a research study about changing environments in northern Canadian communities and was distributed to local people. Three challenging issues in the video creation process including communica-tion issues, technical difficulties and contextual debate were resolved among the supporting agencies and researchers. Conclusions. Dissemination of findings is a crucial step in the research process. Whiteboard animation video products may be a viable and culturally-appropriate form of relaying research results back to Indigenous communities in a storytelling format.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".