Bibliographic record
Abstract
In the December/January 2004-2005 issue of Queue, Roger Sessions set off some fireworks with his article about objects, components, and Web services and which should be used when (“Fuzzy Boundaries,” 40-47). Sessions is on the board of directors of the International Association of Software Architects, the author of six books, writes the Architect Technology Advisory, and is CEO of ObjectWatch. He has a very object-oriented viewpoint, not necessarily shared by Queue editorial board member Terry Coatta, who disagreed with much of what Sessions had to say in his article. Coatta is an active developer who has worked extensively with component frameworks. He is vice president of products and strategy at Silicon Chalk, a startup software company in Vancouver, British Columbia. Silicon Chalk makes extensive use of Microsoft COM for building its application. Coatta previously worked at Open Text, where he architected CORBA-based infrastructures to support the company’s enterprise products.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.029 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".