Bibliographic record
Abstract
The so‐called global land rush has drawn new attention to land, its uses and value. But land is a strange object. Although it is often treated as a thing and sometimes as a commodity, it is not like a mat: you cannot roll it up and take it away. To turn it to productive use requires regimes of exclusion that distinguish legitimate from illegitimate uses and users, and the inscribing of boundaries through devices such as fences, title deeds, laws, zones, regulations, landmarks and story‐lines. Its very ‘resourceness’ is not an intrinsic or natural quality. It is an assemblage of materialities, relations, technologies and discourses that have to be pulled together and made to align. To render it investible, more work is needed. ThisTransactions of the Institute of British GeographersPlenary Lecture uses an analytic of assemblage to examine the practices that make up land as a resource. It focuses especially on the ‘statistical picturing’ devices and other graphic forms that make large‐scale investments in land thinkable, and the practices through which relevant actors (experts, investors, villagers, governments) are enrolled. It also considers some of the risks that follow when these large‐scale investments land in particular places, as land they must.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".