Bibliographic record
Abstract
The faux-Chinese blessing or curse, may you live in interesting times, depending on how one perceives it, unquestionably applies to the remarkable life of Mariana Rubchak.She describes many of those times in her published memoir "My Life with the Poet and His Poetry" and in more detail in a much longer unpublished version, "Those Were the Days: Reminiscences," soon to be deposited in her and her husband Bohdan's papers at the Bakhmeteff Archive at Columbia University.In the Anglophone world, Mariana was known professionally as Marian, while I always preferred calling her by the Ukrainian version of her name.While her married last name Rubchak connects her inescapably to her eminent poetscholar husband Bohdan, she certainly gained equal prominence on her own with an extraordinarily successful career as a scholar in the fields of history and Ukrainian women's studies.Mariana was somewhat of a late bloomer, the result of familial circumstances.She raised four daughters, who were born during her first 1 I am grateful to Mariana's daughters Lesia Kmetyk and Stacey Burke for sharing biographical and bibliographical information about their mother for this article.The picture included here is one that I took of Mariana at a Fulbright reception in Kyiv, Ukraine in spring 1999.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".