Logística reserva aplicada aos resíduos de informática : uma investigação nas IFES de Sergipe
Bibliographic record
Abstract
This study examines how the reverse logistics is being used in the final destination of informatics equipment and supplies post-consumer at the central campi of federal institutions of higher education in Sergipe: Universidade Federal de Sergipe (UFS) and Instituto Federal de Sergipe (IFS). Specifically, it was sought to: a) measure the degree of knowledge of managers responsible for the management of waste electronic equipment on the legal instruments and programs of the Federal Government aimed at managing these wastes; b) describe the process of managing and disposing of waste equipment and computer supplies in federal institutions of higher education of Sergipe; c) determine the similarities and differences in the disposal process of waste equipment and computer supplies between UFS and IFS; d) identify alternatives to improve the management and disposal process of these wastes in the federal institutions of higher education of Sergipe. As methodological aspects, the study is classified as exploratory, descriptive and qualitative, whose research strategy adopted was the study of multiple cases. Regarding the time dimension, the research is cross-sectional. In data collection were applied semi-structured interviews with the managers of the institutions surveyed. Document analysis and direct non-participant observation were used as other sources of evidence. The results revealed that public managers have low degree of knowledge regarding the concepts, legal instruments and federal government programs aimed at disposal of these wastes. Waste management equipment at both institutions have similar procedures. However, the management of post-consumer supplies is distinct, and the final disposition offered to equipment and computer supplies. It was concluded that reverse logistics is still not used at IFES in Sergipe due to the following factors: i) the inapplicability of sectoral agreements between public and private power (they exist but are not yet in force); ii) the refusal by the specific program of the Federal Government to receive donations of equipment and supplies of the institutions surveyed. Among the possible improvements in this process, it can be listed the elaboration of normative instructions, the integrated action of all responsible sectors and the elaboration of an agreement for the Brazilian federal agencies having as a guiding model the Computers Inclusion Programme.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".