Influence of organizational behavior in the accomplishment of oil & gas industry objectives
Bibliographic record
Abstract
This article aims to examine, through an analysis, the Influence of organizational behavior in the accomplishment of Oil & Gas industry objectives, to this end, the research was guided by a postpositivist, qualitative, documentary approach, with bibliographic design, including literary review to know the state of the art of the categories studied, as well as the collection of information obtained from Nelson, Quick., Armstrong, Roubecas, Condie (2019), Liew (2022), Randstad (2018), Thomas (2021) and Yedlin (2017). The findings demonstrate that the Oil and Gas industry is highly influenced by organizational behavior, and the way in which human resources are managed has a high impact on the achievement of the goals and objectives of the companies in the industry. Evidence was found of how the creation of interdisciplinary and effective work groups create synergy when promoting projects, where different factors that may have an impact on their stakeholders are considered. As a result of the daily work with these teams, it is natural that conflicts and negotiations arise, therefore the leaders of the organization must have tools to manage them effectively, where employees feel heard and the company can continue to comply with its objectives.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".