Challenges in Curating Real-Time Data During a Crisis: The Case of the COVID-19 Pandemic in Alberta
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.modeling and correlations. Word clouds helped us notice that the Premier’s health updates covered more varied subjects about the administration of the province while the speech of the CMOH focused more on the pandemic. TF-IDF analysis also showed that the top keywords from the text of the CMOH were a better indication of major events in the pandemic through which a timeline could be created. With topic modeling we identified 10 topics and tracked them along the pandemic, and we were able to see how the discourse on those topics changed. We ran a two tailed Pearson’s correlation and found a positive relationship between positive emotion in the public health briefings by the CMOH and the positive emotion on Twitter and in new articles. Even though data collection had to be started immediately with little planning, preliminary data analysis showed that the data collected has much research value and can inform interested researchers about the pandemic in Albert. The data was cleaned and deposited in a public repository.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".