Peer Review #1 of "An ethical visualization of the NorthCOVID-19 model (v0.2)"
Bibliographic record
Abstract
When modelling epidemics, the outputs and techniques used may be hard for the general public to understand.This can cause fear mongering and confusion on how to interpret the predictions provided by these models.This article proposes a solution for such a model that was created by a Canadian institute for COVID-19 in their region-namely, the NorthCOVID-19 model.In taking these ethical concerns into consideration, first the web interface of this model is analyzed to see how it may be difficult for a user without a strong mathematical background to understand how to use it.Second, a system is developed that takes this model's outputs as an input and produces a video summarization with an autogenerated audio to address the complexity of the interface, while ensuring that the end user is able to understand the important information produced by this model.A survey conducted on this proposed output asked participants, on a scale of 1 to 5, whether they strongly disagreed (1) or strongly agreed (5) with statements regarding the output of the proposed method.The results showed that the audio in the output was helpful in understanding the results (80% responded with 4 or 5) and that it helped improve overall comprehension of the model (85% responded with 4 or 5).For the analysis of the NorthCOVID-19 interface, a System Usability Scale (SUS) survey was performed where it received a scoring of 70.94 which is slightly above the average of 68.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.031 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.310 | 0.161 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".