What are the benefits for the municipality of Santa Maria-RS resulting from the resources income from other capital costs (OCC) by the units of the Brazilian Army sediated in the municipality?
Bibliographic record
Abstract
One of the important sources of revenue for a municipality is the resources coming from other federal entities, for example those that are allocated through the federal public budget. These resources may come from public policies, compensation for tax revenues generation, payment of public servants' remuneration, or, still, expenses related to the public agencies creation or maintenance in a given location, for instance the expenses necessary to maintain the vegetative life of a specific organ, which is the case of resources from other costs and capital (OCC). Considering the city of Santa Maria-RS, which has one of the largest universities in the country, the second largest military contingent in Brazil, in addition to several other federal agencies, these resources are strategic for the municipality's economy. Furthermore, the city has a low level of industrialization, that makes this type of resource more crucial. In this case study, all commitment notes issued by all Brazilian Army Management Units (UG) and Military Organizations (OM) based in the municipality were verified, in order to analyze the destination of these resources and their impacts in Santa Maria, as well as measuring the losses caused by her evasion. It was observed that, of all the resources that directed to Santa Maria in the studied period, through the military organizations of the Brazilian Army, as OCC, about a quarter of them remained in companies in the municipality, which represented 0.214% of the municipality's GDP. Meanwhile, about three quarters of these resources were committed and spent in other municipalities, which represented an evasion of about 0.552% of the municipality's GDP. The same occurred in relation to the possibility of creating jobs in the municipality. Considering this, even though there is a tenfold risk of incurring canceled leftovers registration, therefore, resources to be lost, in companies outside the municipality. The loss of direct collection, through services tax, did not represent an incredibly significant percentage, since the municipality collected about three quarters of this type of tax.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".