Bibliographic record
Abstract
This is the biggie, the paper that made my name (and to a lesser extent Ward's, since he was already a little famous). I got my name first on the paper because of our rule that whoever wrote the first draft of a paper got to put his name first. This has caused considered confusion since then, with people crediting me as the inventor of CRC, or telling Ward how exciting it must have been for him to work with me. There are a couple of stories about this paper. First, the title. Ward and I wanted to be very careful not to claim more for CRC than we could prove. We knew it was good for teaching “object-think,” so that's the approach we took in the paper, the one single point we pushed. We deliberately understated what we thought the impact would be, leaving it to our readers to extrapolate to making CRC cards into a design or analysis technique. We spent at least an hour on the phone polishing the title, and the result pleases me as much now as it did then. Another story- I missed OOPSLA '89, waiting for the arrival of Lincoln Curtis, child number two. I had no idea of the impact of this paper. Then I attended OOPSLA '90 in Ottawa. I was in a “Design and Analysis” BOF. The biggies were all there: Booch, Constantine, Yourdon.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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".