Bibliographic record
Abstract
On February 24, 2022, Russia launched a full-scale invasion of Ukraine that dominated headlines around the world. Millions of Ukrainians would flee the country, and a third of the population would be displaced. In the days following the invasion, Swedish migration expert Gregg Bucken-Knapp sent text messages to his Ukrainian colleagues, offering support and assistance. These were their responses. In a series of graphic vignettes, Messages from Ukraine takes the words of Ukrainian migration professionals and transforms them into snapshots of how war affects the lives of everyday people: those who are forced to flee home and seek safety elsewhere, those who choose to stay and volunteer or fight, those who witness events unfolding from afar, and those who find themselves trapped in cities under siege. Messages from Ukraine captures a moment in time to tell a timeless story about war, displacement, determination, and resilience. Proceeds from the sale of Messages from Ukraine will go to the Canada-Ukraine Foundation, a national charitable foundation that provides humanitarian aid to the people of Ukraine.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.261 | 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; both teacher heads agree on what is shown here.
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".