Poetics and politics of remembering childhood in Romanian post-communist fiction
Bibliographic record
Abstract
The authors explore the representations of the child and childhood which emerged in post-communist Romania, with a clear focus on literary works. The paper proposes a triadic typology of literary remembrance by distinguishing between the allegorical, experiential, and metamodern modes of remembering childhood. They also assess the political implications of Romanian post-communist writers’ strategy of revisiting a troubled past from the child’s perspective by examining the connections between the narrative point of view and the issue of political agency. Their research reveals the complex relation between remembering childhood and remembering communism, one that indicates the authors’ attitude toward the past. Each mode of remembering childhood is analyzed in novels written by Romanian and Romanian-born authors: a) the fantastic and allegorical chronicle of Romanian communism, using largely the child’s narratorial perspective, in Mircea Cărtărescu’s and Herta Müller’s works; b) the experiential mode of remembering and fictionally representing children’s social roles and their traumatic experiences in Radu Pavel Gheo’s and Nicolae Avram’s novels; and c) the metamodern based on a violent and post-ironical view of the world in Aglaja Veteranyi’s and György Dragomán’s novels. Child characters often take a stand or subversively react to the system, showing the authors’ critical engagement with the past.
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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 | 0.007 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".