Bibliographic record
Abstract
Geographical scholarship has rightly problematised the act of archival research, showing how the practice of archiving is not only concerned with how a society collectively remembers, but also forgets. As such, the dominant motif for discussing historical methods in geography has been through the lens of absence: the archive is a space of 'traces', 'fragments' and 'ghosts'. In this paper I suggest that the focus on incompleteness and partiality, while true, may also belie what many geographers working in archives find their greatest difficulty: an overwhelming volume of source materials. I reflect on my own research experiences in the pacifist archive to suggest that the growing scale and scope of many collections, along with the taxing research demands of transnational perspectives, pose immediate practical challenges for geographers characterised as much by abundance as by absence. In the second half of the paper, drawing on recent scholarship in history and geography, I argue that the method of biography offers one possible strategy for navigating archival abundance, allowing geographers to tell stories that are wider, deeper and more revealingly complex within the existing time and financial constraints of humanities research.
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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 | 0.126 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.014 | 0.155 |
| Scholarly communication | 0.030 | 0.053 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".