Unpacking complexity in public health interventions with the Actor–Network Theory
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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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.055 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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 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".