Linking process and effects of intersectoral action on local neighbourhoods: systemic modelling based on Actor–Network Theory
Bibliographic record
Abstract
After 25 years of intersectoral practice to increase health promotion resources, there is little scientific literature linking analysis of processes to observation of effects. Applying Actor-Network Theory, this article examines how the effects of intersectoral action are produced and can be attributed to its processes. A prospective multiple case study (2013-2016) was conducted on Neighbourhood Committees (NCs) in Montreal (Canada). Three NCs were studied using four kinds of data: direct observation notes of meetings and events, documents, logbooks and interviews. Systemic modelling of local intersectoral action was used for data collection and analysis. The results show that the transformations in living environments were produced by sequences of a limited number of 'transitory outcomes' that mark the progression of intersectoral action up to its effects. The list of transitory outcomes identified make up three functions in the production of change: (i) network setup and governance; (ii) self-representing and influencing others; (iii) aligning necessary actors and resources. The production of effects follows a systemic model wherein unique configurations of transitory outcomes, adapted to the different contexts where interactions are occurring, represent the change processes that lead to the effects.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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 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".