Exploring the Intervention— Context Interface
Bibliographic record
Abstract
It has been acknowledged for several decades that programs interact with context. The nature of this interactivity, and how it defines a program, has not been adequately addressed. We view this lacuna as a function of the dominant theoretical perspectives guiding knowledge of program operations. We propose the actor-network theory (ANT) and its conceptual apparatus, the sociotechnical network, as suitable for guiding the acquisition of general knowledge on program operations. We tested this proposition with an instrumental case study of health professional practices during the implementation of a nutrition program into an elementary school setting. Data collection and analysis were guided by the ANT. Data were derived from semistructured interviews completed with six health professionals (nutritionists). Analysis procedures focused on the nutritionists’ collective representation of the microprocesses by which they aimed to build a sociotechnical network of alliances with educational stakeholders. Findings identified nutritionists as preoccupied with three overarching goals during the implementation of the nutrition program, whereby goals were found to take form interactively with the interests of the program participants (primarily students) and stakeholders (primarily teachers). Nutritionists strategically translated program components as a means of negotiating with participants and stakeholders. The findings of this study support the theoretical proposition that program implementation is a process of expanding a sociotechnical network. Beyond simply reaffirming that programs do indeed adapt to context, we interpret this adaptation through the lens of a social theory that suggests why and how adaptation is an inevitable component of program implementation.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".