Landscape analysis of prominent investors in the Australian food industry
Bibliographic record
Abstract
OBJECTIVE: Large investors have the potential to help address unhealthy diets by influencing food company nutrition practices. This study aimed to identify prominent investors in Australia's food industry and examine how they consider nutrition as part of investment decisions. METHODS: Leading companies and their shareholders in food manufacturing, grocery retail, and fast-food sectors were identified using the Passport and Orbis databases. A desktop review of policies and voting decisions for the top 10 investors per sector was conducted from May-August 2024. RESULTS: A small number of investors hold substantial shares in Australia's food industry. United States-based investors (e.g., BlackRock and Vanguard) were prominent shareholders across all sectors, with HSBC Bank holding substantial ownership in grocery retailers. Most investors did not disclose investment-related nutrition policies, and typically voted against shareholder proposals related to nutrition and health issues. CONCLUSIONS: Prominent investors have the potential to shape Australia's food environment, but they do not currently prioritize nutrition as part of investment decisions. Increased disclosure of investor policies, proxy voting records, and investor engagements with companies may provide opportunities to improve public health. IMPLICATIONS FOR PUBLIC HEALTH: Targeting large investors and the regulatory environments under which they operate may contribute to efforts to improve population diets.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".