Intuitive versus theoretical approaches to visual communication to facilitate knowledge transfer
Bibliographic record
Abstract
During the start of the 2002-2003 Ontario youth hockey season, Hockey Canada lowered the age of allowable bodychecking from 12-13 years of age to 9. Dr.Michael Cusimano, a neurosurgeon from St. Michael’s hospital, investigated the neurological impact of this rule change on youth players in an effort to educate hockey parents on the dangers of bodychecking. Using Dr. Cuismano’s research data, the investigator created three information graphics through three different design approaches: intuitive, theoretical and content-theoretical. Through a 5-Step practical-based methodology, the investigator sought to understand whether Dondis’ basic elements of design and Gestalt theory would guide the design process to create a visual solutions geared towards educating hockey parents. The theoretical checklist played played an important role in the creation of the theoretical and content-theoretical designs. Furthermore, the process determined that richness of data generated more robust design solution. When comparing the three designs, it is evident that there is a continual evolution, with each new design extracting strong graphical elements and colour schemes from its predecessor. A blind test was conducted on Dr. Cusimano to determine the success of the visual solutions for the intended target audience. Selected designs included the intuitive and content-theoretical solutions, which Dr. Cusimano felt best represented his research and effectively captured the attention of hockey parents. This experimental design provides a solid foundation, which can be taken further; the three recommendations made by the investigator are to experiment with data-driven parameters, examine the impact of culture on the information design process or hold a focus group with hockey parents to test the impact of the three information graphics created.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".