Bibliographic record
Abstract
A physical geographers’ scope of practice is not defined by any regulatory or academic organization, so perception of the potential contribution of physical geography to sustainability research has been nebulous or informal, at best. In order to understand what physical geographers can do to enhance sustainability, this paper describes a systematic review of peer-reviewed research on sustainability published in three physical geography journals. The results show that physical geographers are active in sustainability research in terms of a spatial perspective, an understanding of human interactions with the environment, and an ability to recognize, interpret, and project environmental change and its impacts. The depth of this understanding is facilitated by a physical geographers’ understanding of the natural world, process and system concepts, the ways that systems are linked and interact, and a willingness to deploy a wide range of methodologies to secure that knowledge. The expertise of physical geographers makes an important contribution to sustainability research and should be considered when multidisciplinary teams are assembled.
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 | 0.167 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".