Evaluating Service Organization Models
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
Based on the example of the evaluation of service organization models, this article shows how a configurational approach overcomes the limits of traditional methods which for the most part have studied the individual components of various models considered independently of one another. These traditional methods have led to results (observed effects) that are difficult to interpret. The configurational approach, in contrast, is based on the hypothesis that effects are associated with a set of internally coherent model features that form various configurations. These configurations, like their effects, are context-dependent. We explore the theoretical basis of the configuration approach in order to emphasize its relevance, and discuss the methodological challenges inherent in the application of this approach through an in-depth analysis of the scientific literature. We also propose methodological solutions to these challenges. We illustrate from an example how a configurational approach has been used to evaluate primary care models. Finally, we begin a discussion on the implications of this new evaluation approach for the scientific and decision-making communities.
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.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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