Bibliographic record
Abstract
This paper is produced to support my thesis show Muta Poesis. I have over the last two years sought through the use of photography to visualize the subject of death. I attempt to capture the emotive aspects of the subject of death, achieving this through the loss of natural daylight which is only apparent within the final photographic print. Within this paper I put forward the research that has become the basis for my studio practice. I explore the subject of death from the Medieval period through to the Victorian period, covering the historical evolution of death imagery and photography. I also explore contemporary death photography, in order to better situate my own photographic work within contemporary art practices. In conclusion of the paper I explain the influences on my understanding and exploration of death related imagery.Dept. of Visual Arts. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .B97. Source: Masters Abstracts International, Volume: 42-04, page: 1085. Adviser: Vladimir Spicanovic. Thesis (M.F.A.)--University of Windsor (Canada), 2003.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.033 |
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".