User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection
Bibliographic record
Abstract
OBJECTIVES: To review user signal rating activity within the Canadian Network for Public Health Intelligence's (CNPHI's) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating? METHODS: KIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff's alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating. RESULTS: The 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal's 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once. DISCUSSION: A diverse community of raters participated in rating the signals. Krippendorff's Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals. CONCLUSIONS: Diversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal's 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".