Assessing the health impact of transnational corporations: its importance and a framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The adverse health and equity impacts of transnational corporations' (TNCs) practices have become central public health concerns as TNCs increasingly dominate global trade and investment and shape national economies. Despite this, methodologies have been lacking with which to study the health equity impacts of individual corporations and thus to inform actions to mitigate or reverse negative and increase positive impacts. METHODS: This paper reports on a framework designed to conduct corporate health impact assessment (CHIA), developed at a meeting held at the Rockefeller Foundation Bellagio Center in May 2015. RESULTS: On the basis of the deliberations at the meeting it was recommended that the CHIA should be based on ex post assessment and follow the standard HIA steps of screening, scoping, identification, assessment, decision-making and recommendations. A framework to conduct the CHIA was developed and designed to be applied to a TNC's practices internationally, and within countries to enable comparison of practices and health impacts in different settings. The meeting participants proposed that impacts should be assessed according to the TNC's global and national operating context; its organisational structure, political and business practices (including the type, distribution and marketing of its products); and workforce and working conditions, social factors, the environment, consumption patterns, and economic conditions within countries. CONCLUSION: We anticipate that the results of the CHIA will be used by civil society for capacity building and advocacy purposes, by governments to inform regulatory decision-making, and by TNCs to lessen their negative health impacts on health and fulfil commitments made to corporate social responsibility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it