On the Notions of Abstraction, Consistency, and Design in the ODP Framework of Viewpoints
Bibliographic record
Abstract
ion, Consistency, and Design in the ODP Framework of Viewpoints Kazi Farooqui, Luigi Logrippo, Department of Computer Science, University of Ottawa, Ottawa K1N 6N5, Canada. (Internet: farooqui|luigi@csi.uottawa.ca) Abstract One of the most fundamental systems analysis and design principle is that of "abstraction". Essentially, the purpose of abstraction is to clarify or highlight some features of a problem by concealing others. The set of viewpoints identified in the ODP architecture is merely a pragmatic classification of concerns. A viewpoint leads to a representation of the system with emphasis on a specific set of concerns, and the resulting representation is an abstraction of the system, i.e., a description which recognizes some distinctions that are relevant to the concern and ignores others. The viewpoint models exhibit very subtle concepts with respect to the notion of abstraction and consistency between them. They offer a very powerful structuring paradigm suitab...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".