Bibliographic record
Abstract
What drew me to geography as an undergrad was the strong environmental component, which covered both science and social science, in the department at the University of Toronto.Once I started, I became very interested in the range of methodological approaches that geographers were using to look at a far wider range of problems than I thought possible in one discipline.In geography, I found people using standard statistical tools and mathematical models based in differential equations and matrix algebra, but I also found other geographers who were using analyses based on abstract algebra and other types of mathematics not normally found outside of that discipline.Philosophically, geographers were quite sophisticated, not only well-versed in Marxism, but I also found geographers who were well-versed in the phenomenologists, Hegel, Wittgenstein and Heidegger.In addition to being well versed in these mathematics and philosophers, geographers were also integrating them in their analyses.I was often directed into an area of study by a direct supervisor or when an opportunity emerged that met my qualifications.Certain legacies, such as COBWEB, the software built to study complexity, were my own idea, but may have been inspired by the work of other scientists.New work in some areas is now driven by student interests as much as my own.The application of COBWEB to the synapse, Alzheimer's disease, malaria, lock-in theory and international conflict were all student ideas.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.315 | 0.140 |
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".