Bibliographic record
Abstract
It was Turgenev who taught Chekhov that, in an age of censorship, objectivity could have the force of polemic without overtly disclosing its critical intents. In the appropriate rhetorical situation, objectivity can function as a mode of “hidden polemic,” a crucial part of the weaponry of the ironist. Even if the objectivity of Turgenev’s presentation did not always guarantee that provocative voices and attitudes would evade the censor’s pen, the mask of objectivity was a subtle rhetorical tool for any writer concerned with social and political criticism.2 Does Ivanov need to be recognized as a play exploiting the rhetoric of objectivity? Can one deploy a rhetoric of objectivity and yet remain committed to an ethics of objectivity? In polemical terms, objectivity has it limits, since, functioning as an ironic mode, its “non-statement” is a severe form of understatement – and, as such, subject to the vagaries of indeterminacy. It is the threat of such indeterminacy, I think, that leads Chekhov to create a play in which the objective is always in tension with the grotesque and the satirical: but understatement counterbalanced by statement still runs the risk of remaining indeterminate.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".