Geographic Information Systems and Book History
Bibliographic record
Abstract
Abstract Scholars working in the multidisciplinary field of book history pose diverse research questions, work with numerous sources of data and information, and employ a variety of analytical methods and tools. Geographic questions have been considered by book historians, notably since the groundbreaking work of Lucien Febvre and Henri-Jean Martin in the 1950s. Geographic information systems (GIS) technology, which was developed in Canada in the 1960s, was initially devised to support new methods of analysis and visualization in the physical and life sciences relating to spatial conditions, patterns, trends, and projections. Since the late 1990s, social scientists have used GIS increasingly, and, since the early 21st century, humanities scholars have also begun to use GIS as a result of digital and spatial turns within their fields. The application of GIS as an analytical method to investigate research questions in book history, first suggested in 1997, is now employed across a range of scholarly endeavors. Examples from the sciences that illustrate the required data structures, as well as the scope and analytical power of GIS, illuminate the development of geographies of the book. Such examples also illustrate the types of questions for which GIS is appropriate for advancing knowledge. Limited training for book historians in the application of GIS, along with the complexities of the technology, have resulted in the need for partnerships with quantitative researchers. These collaborations are increasing understanding of the spatial dimensions of book and print history. In addition, new programs of study in digital humanities, and initiatives of innovative scholarly societies, are helping to forge a generation of technologically trained scholars to propel the field of book history further.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".