A managerial approach in resource allocation models: An application in US and Canadian oil and gas companies
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".