Bibliographic record
Abstract
Data Envelopment Analysis (DEA) is a non-parametric frontier approach used both to model production processes and/or production organisations of goods and services (public and private) as inputs/output systems and to measure their relative efficiency. However, in addition to being an instrument for measuring economic performances, the DEA is also used in its multiplicative version as a policy tool to support managerial decisions for the pursuit of competing objectives. Based on the data, the DEA offers an answer to the pursuit of competing objectives by placing it as a trade-off and calculating the optimal weights associated with each of them. Here, we will address two questions: 1) how to overcome the DEA modelling of decision-making units as "black boxes" that use inputs to be translated into outputs to taking into account the operations/stages involved in this transformation process, and 2) how to use the Network Data Envelopment Analysis (NDEA) approach as a policy tool. In particular, we will propose a way to use a relational NDEA model as a policy tool by exploiting the possibility of making assumptions about the model variables. In our opinion, compared to the standard DEA, the advantage of using the NDEA as a policy tool is that the policy objectives (in this case organisational) can also be disaggregated at the sub-process level. In particular, we will propose to translate the system of organisational objectives into an NDEA model as a mix of "discretionary/non-discretionary" assumptions about the variables of the model itself. To clarify our proposal, we will then develop an application in the public health services sector.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".