Bibliographic record
Abstract
This chapter, focusing comparatively on the Canadian and UK experiences, explores one particular component of the wide-ranging work involved in privatizing and commodifying public land: the discursive component. It turns to a context where land commodification is driven less by extra-economic force and more by the lure of economic efficiency. The chapter examines the land “fictions” or legitimizing narratives — not just about land per se but about the different types of owners it can have — to rationalize and justify the process of commodification. It reveals that the kernel of these fictions is the particular idea invoked by the state that public land is often “surplus” land, and thus free to be commodified. The chapter details how surplus labels are readied, and land released to the private sector, through techniques of (dis)incentivization, the normalization of public land disposal practices, and the transfer of authority to different actors. Ultimately, the chapter presents three main sections: some essential preparatory material, the pivotal concept of “surplus,” considering its distinctive articulation and coloring in each national context, and the ways in which these fictions of surplus are brought to life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".