The Financialization of Farming: The Hancock Company of Canada and its Embedding in Rural Australia
Bibliographic record
Abstract
Abstract This chapter examines the involvement of finance companies in the purchasing and leasing of Australian farmlands. This is a new global phenomenon as, in past decades, finance companies have lent money to farmers, but have rarely sought to purchase land themselves. We investigate and discuss the activities of the Hancock company – an asset management firm that invested in farmland in northern NSW. Material on the activities of Hancock and other investment firms were obtained from documents on the public record, including newspaper reports. Semi-structured interviews with community members were conducted in the region of NSW where Hancock operated. Australian agriculture is being targeted for investment by companies in the finance industry – as part of a growing ‘financialization’ of farming. While it is financially beneficial for companies to invest, they do not do so in ‘empty spaces’ but in locations where people desire to live in a healthy environment. The Hancock company was criticized by community residents for failing to recognize the concerns of local people in pursuing its farming activities. To date, there have been few studies on the financialization of farming in Australia. By investigating the operations of the Hancock company we identify a number of concerns emerging, at the community level, about an overseas company running Australian-based farms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".