Research in the spatial sciences: how are Canadian geographers contributing?
Bibliographic record
Abstract
Geographic data collection, manipulation, analysis and visualization options have experienced substantial improvements during the past several decades, largely spurred by advancements in computing capabilities. While geographers are often credited with identifying and expanding many of the emerging application areas and innovations for the analysis of spatially (and sometimes temporally) referenced data, we are specifically interested in the role of Canadian geographers in the rapidly evolving domain of spatial science. We pose the following provocative question with the intent of not only summarizing the Canadian literature, but also to stimulate an informed discussion: ‘are Canadian geographers developers or users of spatial analytical methods?’ We review the refereed literature from 1980 to 2008 to describe the nature of contributions by Canadian geographers, beginning at about the time of widely accessible computing (1980s). Our summary broadly classifies subdisciplinary contribution areas as being best described as GIS, remote sensing, or spatial statistics, while each contribution area may take the type of algorithm development, advancement and synthesis of theory, or the application of existing methods. We paint a picture of the current contribution landscape and reflect on significant achievements while commenting on some potential weakness that with increased resources and focus might become future realms of advancement.
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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.066 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".