Bibliographic record
Abstract
All geography is historical geography. There is no better way to weaken our scholarly power than to engage in presentism, either by ignoring or by discounting the past. It is equally problematic to engage in historicism, however, as though the past can be excavated for its own sake. The present is but the past becoming. Answering the charge from the AAG's Historical Geography Specialty Group organizers to reflect on the significance of archival work for historical geographers, I have divided this discussion into three sections, which roughly coincide with my own trajectory from graduate school, through an early, and now a late, academic career. Although that trajectory is roughly chronological, it is also a journey of a changing relationship with archives, and with the people whose lives are archivally represented. This article begins with my research in a Japanese village during the early 1980s, proceeds to discuss the formation of a career based on studying the development of Japanese-Canadian communities, and concludes with a brief description of current collaborative work in the Downtown East Side of Vancouver.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.073 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".