Bibliographic record
Abstract
Many geographers, past and present, have addressed public policy issues facing nations and peoples and in the process offered solutions to highly complex problems. Three ‘sentinels’ of the discipline, Halford Mackinder, Carl Sauer and Thomas Griffith Taylor, served as protectors of geography speaking up for the science in a way often confronting public officials, politicians and others. They contributed significantly to the development of geography in Britain, the USA, Australia and Canada, while engaging in public policy debates on topics such as geopolitics, geographical constraints on land use and natural resource management. All three were advocates for the unity of geography, stressing how an understanding of the interconnectedness of natural and human phenomena can assist in decision making. They were often frustrated by what they saw as ill-informed policies which did not respect geographic realities. Given their varied contributions, it is difficult to fully assess their impact both during their long and productive lifetimes, and subsequently, especially given the interdisciplinary and contested nature of their research. Today, academic geographers are faced with having to increasingly ‘prove the impact’ of their research, something beyond the comprehension of previous generations. Lessons from an analysis of the work of these ‘sentinels’, as well as my own experience, show how difficult a task this will be.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.093 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".