Unpacking complexity in public health interventions with the Actor–Network Theory
Why this work is in the frame
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Bibliographic record
Abstract
This article proposes a sociologically informed theoretical and methodological framework to address the complexity of public health interventions (PHI). It first proposes three arguments in favour of using the Actor-Network Theory (ANT) for the framework. ANT: (1) deals with systems made of human and non-human entities and proposes a relational view of action; (2) provides an understanding of the intervention-context interactions and (3) is a tool for opening the intervention's black box. Three principles derived from ANT addressing theoretical problems with conceptualisation of PHI as complex systems are proposed: (1) to focus on the process of connecting the network entities instead of their stabilised form; (2) both human and non-human entities composing networks have performative capacities and (3) network and intervention shape one another. Three methodological guidelines are further derived: (1) the researcher's task consists in documenting the events that transform the network and intervention; (2) events must be ordered chronologically to represent the intervention's evolution and (3) a broad range of data is needed to capture complex interventions' evolution. Using ANT as a guide, this paper helps reconcile technicist and social views of PHI and provides a mean to integrate process and effect studies of interventions.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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