Bibliographic record
Abstract
In an era when science was outpacing widely accepted superstitions to explain the unknown, a subsection of people sought to use scientific methods and technology to make the unknown known and prove the existence of that which is beyond human perception: the supernatural. The pseudo-intellectual movement born of this goal came to be known as spiritualism. A line can be drawn between these two concepts, with spirit photography branching out from the spiritualist movement. From the mid-19th century to the early 20th century, audiences were enraptured by portraits seemingly haunted by transparent apparitions transposed over the image. This trend came to be known as “spirit photography.” Beyond its use for documentation, photography was derived from visual art techniques to create narratives or exaggerate aesthetic beauty. In a broad sense, photography was used as a tool of deception, and spirit photography spent decades effectively convincing its audience of the existence of spirits. In this paper, I explore the reasons belief in spirits through spirit photography persisted in spite of the 19th century’s prevailing fixation on rationality. Through the analysis of spirit photographs from the Toronto Public Library’s Arthur Conan Doyle collection, I discuss the techniques photographers used to produce ghostly images by contextualising spirit photography within the historical circumstances that led to its formation. Following the analysis of spirit photographs, I discuss why these techniques impacted their early 20th century audience, who became resistant to the increasing arguments against their validity.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".