A managerial approach in resource allocation models: An application in US and Canadian oil and gas companies
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
In resource allocation and target setting problems, a central decision makers? managerial standpoint has a pivotal role, especially when we encounter undesirable outputs such as the greenhouse gas (GHG) emissions. In such circumstances, firms have to cooperate with each other, to achieve the central planner?s aims. Looking into literature reveals that the existing resource allocation models based on data envelopment analysis (DEA) have not aptly considered the influence of managerial efforts and technological innovations in this sense. This study proposes a centralized model incorporating managerial disposability. This model not only reflects the leadership performance of the central planner and the technological novelty perspective in the resource allocation and target setting problem, but also has a positive modification against an environmental adaptation change. In order to illustrate the applicability of our resource allocation and target setting model, a case study of 23 US and Canadian oil and gas companies has been conducted. Analysis of the results reveals the appropriacy and efficiency of our proposed model in dealing with the current perspectives concerning the issue of resource allocation and target setting.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it