Bibliographic record
Abstract
From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.The social mission of information professionals is to provide society with high quality information storage and retrieval services. In order to fulfill this mission, the professionals need to have an understanding of the tools they use that is sufficiently thorough for predicting the behavior of these tools in all normal circumstances. One important class of tools used by information professionals are the textual database management systems (TDBMS's). At present, the retrieval capabilities of these systems are almost without excep- tion incompletely described, a situation which sometimes renders the accurate prediction of their behavior dicult. Thus, the quality of in- formation storage and retrieval services that the body of information professionals can provide society is not as high as it could be. To cor- rect this situation, the retrieval behavior of the TDBMS's available to information professionals must be precisely and exhaustively described. In other words, an abstract retrieval model has to be elaborated for them.In this paper, we present what we believe to be the ?rst formally de?ned abstract retrieval model especially designed for describing the retrieval behavior of common, everyday TDBMS’s. The model is rigorously de?ned and can be used as a basis for describing the retrieval behavior of most of the existing TDBMS’s that use boolean logic and so-called “repeating”values. . The process of modeling the retrieval behavior of TDBMS’s shows that the form of query expressions accepted by existing systems is fairly restricted, and suggests a possible (and easily implementable) generalization. We show that this generalization would not only allow the formulation of interesting and meaningful requests that are impossible (or very dif?cult) to formulate in the present systems, but would in fact grant logical completeness to retrieval languages, a form of completeness analogous to relational completeness in the relational model.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".