Ukraine calling a kaleidoscope from Hromadske Radio 2016-2019
Bibliographic record
Abstract
This book is like a time capsule containing a selection of interviews that aired on Hromadske Radio's Ukraine Calling show. They capture what people were thinking during a critical time in the country's history, from the July 2016 NATO Summit through to Volodymyr Zelenskyy's 2019 landslide election victories. Decision makers, opinion makers, and other interesting people commented on events of the day as well as larger issues. Topics range from politics to sports, religion, history, war, books, diplomacy, health, business, art, holidays, foreign policy, anniversaries, public opinion to freedom of speech. Interview guests include Canada's then Foreign Minister Chrystia Freeland, writer Andrey Kurkov, Crimean political prisoner Hennadii Afanasiev, who was tortured in 2014, Ukraine's acting Health Minister Ulana Suprun, American analyst/journalist Brian Whitmore, UNHRC's Pablo Mateu, ethnologist Ihor Poshyvailo, investment banker Olena Bilan, Tufts University's Daniel Drezner, a cameo appearance by Boris Johnson, and many more. Together these interviews provide a unique, diverse, and kaleidoscopic perspective conveying the substance, atmosphere, and flavor of Ukraine while it was on the receiving end of a hybrid war from Russia
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".