The value of business intelligence in the context of developing countries.
Bibliographic record
Abstract
In the corporate context, a combination of constant technological innovation and increasing competitiveness makes the management of information a huge challenge and requires decision-making processes built on reliable and opportune information, gathered from internal and external sources. Although the volume of information available is increasing, this does not mean that people are able to derive value from it. Regarding IT, after years of important investments in order to put in place a technological platform that supports all business processes and that strengthens the efficiency of the operational structure, most organizations are supposed to have reached a level where the implementation of IT solutions for strategic levels becomes possible and necessary. This context explains the emergence of the domain generally known as “business intelligence” (BI), seen as an answer to the current needs in terms of information for decision-making with the intensive utilization of information technology. The objective of this research project is to examine the meaning and role of BI in a particular context, one of developing countries, more specifically, in Brazil. If the management of IT is a challenge even to companies in developed countries, what can we say about organizations struggling in unstable contexts such as developing ones?
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".