Canadian Magazines and Their Spatial Contexts: Digital Possibilities and Practical Realities
Bibliographic record
Abstract
The digitization of literary texts and periodicals brings with it exciting possibilities, including the ability to create visualizations of places and trajectories using mapping technologies. However, such mapping also has the potential to be somewhat perilous, as researchers need to invest significant amounts of time without always being certain in advance about the intellectual benefits that will result. In this article, I ask what it means to map place in relation to little magazines—places of publication, places mentioned, places whose broader imaginative pull is attested to by depictions of travel and tourism—and consider not only how but also why and when it is worth going to the trouble of geocoding texts from literature, literary history, and book history. I take several case studies of digital projects which use mapping of various sorts to explore what can be discovered from geographical and other forms of visualization, and I suggest particular kinds of data, and text, that are especially beneficial to bring within the ambit of this kind of methodological approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.028 | 0.032 |
| Scholarly communication | 0.029 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".