Nineteenth-Century Canadian Photographically Illustrated Books Online: Examining and Sharing a Research Collection as Data
Bibliographic record
Abstract
This thesis investigates the artistic, economic, and social history of original photographs as book illustrations in Canada, from their first appearance in Samuel McLaughlin’s monthly periodical, The Photographic Portfolio: A Monthly View of Canadian Scenes and Scenery (Quebec: S. McLaughlin, 1858-1860), through the waning years of the nineteenth century when halftones and other forms of photomechanical prints surpassed them as more commercially viable options. The accompanying digital collection at Nineteenth-Century Canadian Photographically Illustrated Books (https://canadianphotographicallyillustratedbooks.com) brings to light hundreds of photographs found in books which survive in public collections today, primarily in Ontario and Quebec, and documents the often surprising differences between book copies as well as relationships to other nineteenth-century media. Thesis chapters present case studies of McLaughlin’s Portfolio and James MacPherson LeMoine’s Maple Leaves: Canadian History and Quebec Scenery (Quebec: Hunter, Rose & Co., 1865) which explore, in turn, how authors and publishers used photographs and how readers engaged with them. The first chapter looks at the disconnect between the fraught economics of book production and the emerging popular discourse around photographically illustrated books as affordable and desirable artistic goods. By exploring the commercial challenges of publishing in a small, local market, I demonstrate that photographic books were probably far from profitable ventures. Their value was rather in their ability to fulfill the popular interest in illustration, supplying a developing local industry with assuredly high-quality goods. The second chapter explores the previously unknown trend of extra-illustrating with photographs in Canadian books, bringing to light a practice that has not received much attention in the history of photography generally and which offers a new mechanism for understanding the audience for photographic illustration as participants in meaning-making. A critical component of the thesis is the digital collection, which makes available the data that allowed for unique discoveries about the Canadian market for photographically illustrated books, such as the presence of extra-illustrators in the creation of Maple Leaves. The thesis concludes with a reflection on how to present humanities research collections as reusable research data by considering emerging practices from the cultural heritage sector around ‘collections as data.’
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".