Tears of stone and clay: the affect of mourning images in middle-period China
Bibliographic record
Abstract
Abstract Representations of intense emotions are rare in the Chinese visual tradition in comparison with their counterpart in literary convention. While the reasons for this deserve an in-depth interdisciplinary study, such general reservation contrastingly highlights a distinct visual phenomenon that emerged and flourished during the middle period (9th-14th centuries). This time period witnessed a growing number of visual representations of grieving figures in funerary and religious (mainly Buddhist) contexts. By articulating various representational modes of mourning images, this essay discusses a significant development in the emotional lives of middle-period Chinese. Occupying seemingly disparate ritual spaces (the Buddhist pagoda crypt and the tomb) the images of sorrowful mourners conspicuously emerged as an appealing motif for adorning the burial spaces of their deceased. These two sites of intense affect reveal that era's desire for placing the virtual mourner in the space designed for the dead as a visual agency conveying the emotive surrounding the death of the beloved, be they local monks or family members, who often lacked literary means to express their feelings. Recognizing this affective mode helps us to better understand the complex interplay between the emotions, the social and cultural sanctions in expressing them, and the visual codes created thereof, in post-medieval China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".