Bibliographic record
Abstract
In the field of life writing, a collective biography is a biography of a group of lives that share common background characteristics. Han Shaogong’s A Dictionary of Maqiao (1996) collects life stories, in a lexical list of entries, of 22 principal characters from the fictitious village of Maqiao. If there is a common background characteristic of Maqiao’s people, it is their special way of using words to shape their way of thinking. The 115 word entries that Maqiao people use reveal life stories covering more than a century. As a first person narrator and a biographer of collective lives, Han also seems to be a witness to their lives. How does he fuse autobiographical, biographical and historical truths into this text? How does he interpret Maqiao’s lexicon in the light of their collective lives? Are auto/biographical theories applicable to Han’s collective biography? Finally, what contributions does he make to collective life writing? To answer the above questions, this paper takes A Dictionary of Maqiao as a metafiction to discuss life writing issues with theorists such as Paul John Eakin, Philippe Lejeune, and Zhao Baisheng. It also searches for Han’s methodologies and techniques in creating collective life stories through a textual analysis. By reading literary biographies of Han Shaogong and his stories of Maqiao people, this paper also analyzes what constitutes “truths” and “facts” in this collective biography. Finally, it demonstrates how Han makes biography new in terms of life writing.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".