O uso de patentes como fonte de informação em dissertações e teses de engenharia química: o caso da Unicamp
Bibliographic record
Abstract
Acredita-se que as patentes são pouco exploradas como documentos fornecedores de informações; tanto por empresas, quanto por instituições de pesquisa, universidades, etc. Nesse contexto, o presente artigo teve a finalidade de investigar se os documentos de patentes são utilizados como fonte de informação nos trabalhos acadêmicos (dissertações de mestrado e teses de doutorado). Para isso, foram selecionados trabalhos da área da engenharia química, do período de 2000 a 2007, da Universidade Estadual de Campinas (UNICAMP). Os dados foram coletados através das patentes citadas e referenciadas nos trabalhos acadêmicos e a amostra utilizada foi de 586 trabalhos. Os resultados dessa pesquisa evidenciaram que 16,4% dos trabalhos analisados utilizaram patentes como fontes de informação e citaram esse tipo de documento. Além disso, este trabalho indicou que as patentes americanas são as mais citadas por trabalhos da UNICAMP (63,8%). Porcentagens menores ficaram para as patentes japonesas (9,0%), patentes européias (7,2%), patentes inglesas (4,0%), patentes alemãs (3,2%) e patentes brasileiras (2,7%).AbstractIt is believed that patents are not fully explored as information providers documents, both by companies and by research institutions or universities. In this context, this paper aimed to investigate whether patents are used as information sources in academic works (master's dissertations and doctoral theses). For this, works of chemical engineering from 2000 to 2007 of the State University of Campinas, Brazil (UNICAMP) were selected. Data were collected through the patents cited and referenced in academic works and the sample comprised 586 items. The results of this study have demonstrated that 16.4% of the works analyzed have used patents as information sources and have also cited this kind of document. In addition, results indicated that U.S. patents are the most cited by researchers from UNICAMP (63,8%). Lower percentages were observed for Japanese patents (9,0%), European patents (7,2%), English patents (4,0%), German patents (3,2%) and Brazilian patents (2,7%).
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".