Análise do padrão brasileiro de metadados de teses e\ndissertações segundo o modelo entidade-relacionamento
Bibliographic record
Abstract
In the last decades, with the automation of the information systems and with the advent of digital libraries, norms, standards and techniques of the librarianship have been widely argued, analyzed, reevaluated and reorganized. Among these document organizing instruments there are rules and standards destined to the descriptive representation, like the\nMachine Readable Cataloguing Format, MARC, the Anglo-American Cataloguin Rules, AACR and the International Standard Bibliographic Descriptions, ISBD, that actually has been material for theoretical studies having as aim the development of the metadata standards for treatment of information for digital libraries. The objective of that thesis went analyze the pattern metadata Brazilian for Theses and Dissertations, MTD-BR, used in the project of the Digital Library of Theses and Dissertations, sponsored and directed by the Brazilian Institute of Scientific and Technological Information, IBICT, using the methodology of data modeling, in agreement with the Functional Demands for Bibliographical Description, FRBR, proposed by a specific group of studies of the International Federation of the Associations of Libraries, IFLA, based on the model entity-relationship, MER. Initially this methodology was applied\nISBD(G) and the results were presented in the final report of FRBR. Of this work, the same methodology went to base of two studies accomplished by Tom Delsey, of the National Library of Canada, being applied MARC and AACR. In to present thesis, the application of\nthis methodology of data modeling to the pattern of MTD-BR, will be the first initiative in Brazil of application of the methodology of FRBR. This study, presents the potentialities of the data modeling in the construction of libraries that digital. According to this research results, it is possible also to identify some points to be reflected in the development of a new\nversion of the MTD-BR standard, beyond proposals of magnifying of the metadata elements of this standard. Therefore, it was been evidenced to be essential that new studies must be carried out, using the application of this methodology to the other metadata standards, even though analising others tools of data modeling as the one intitled oriented object model, largelly applied for the information system project, considering its interface with librarianship norms, principles and instruments. It would be expected that not only the descriptive representation could be improve with this kind of studies, but also the area of thematic representation.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".