The value of business intelligence in the context of developing countries.
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 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?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it