Leveraging information for high level-of-abstraction organizational processes
Bibliographic record
Abstract
Nowadays, Big Data techniques have made possible to obtain interesting low-level information from large amounts of data.However, the information is often difficult to be enriched enough to aid in high-level organizational processes.The objective of this work is to define a model to allow the use of available information for the design and implementation of high level-of-abstraction processes that take place in the organizations.Particularly, this paper is focused on the strategic planning (SP) process, one of the most complex and abstract processes in any company.So far, there are very few successful attempts to automate this process, which is usually based on manual tasks.At the most, the results from the automatic data analysis are generated and used as reports and are not integrated in the process.A further step is proposed, posing a SP model based on a multi-agent system with a blackboard, which is used as a means of communication and storage of the generated information.The information is described using ontology to formalize both the SP process and the information used in each step.The combination of these elements enables the participation, interaction and sharing of information and knowledge of the participants in the process.The accumulated knowledge allows the use of previous experience to automate the process and improve the decision making.In short, the proposed model is a formal, comprehensive, agile and flexible solution to perform the process of SP in organizations today leveraging of the enormous amount of available data and the gathered experience.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".