‘Remember the peasantry’: A study of genocide, famine, and the Stalinist Holodomor in Soviet Ukraine, 1932-33, as it was remembered by post-war immigrants in Western Australia who experienced it
Bibliographic record
Abstract
In 1936 Joseph Stalin offered what he called ‘friendly advice’ to his Communist allies in war-torn Spain: ‘One should pay attention to the peasantry, which...is of great importance.’1 So important, indeed, was the peasantry to the security of the Communist state that Stalin had adopted ruthless, widespread policies in the Soviet Union to ensure their compliance with the centralised regime in Moscow. The devastating famine experienced in Soviet Ukraine from 1932 to 1933 was an example of such ruthless policies of Stalin’s government. Some scholars argue that in the space of a year up to ten million people died in the famine now known as the Holodomor. This is a research project exploring the history of Ukraine’s Holodomor, exploring popular debate, scholarly research and survivors’ memories. A small émigré community of Ukrainians settled in Western Australia in the post-war years, many of whom either experienced or witnessed the famine and now have harrowing stories to share. This research sought to further explore this relatively unknown famine through examining scholarly debate and primary source evidence, and by conducting interviews with more than forty survivors now living in Perth. The material collated in the interviews contributes significantly to the scholarly and popular comprehension of the Holodomor. When compared to internationally accepted interpretations of the term, and compared to similar studies abroad, it is clear that the events of 1932-1933 were an act of genocide committed against the Ukrainian people. A thesis presented to the University of Notre Dame Australia in fulfilment of the thesis requirement for the degree of Doctor of Arts in the School of Arts and Sciences.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".