Bibliographic record
Abstract
I was honored to receive ACM SIGSOFT's Influential Educator Award1 for 2020 this past April. I was also surprised: while I think I've helped scientists through Software Carpentry and other projects, nothing I've done in the last twenty years seems to have had much influence on software engineering. It isn't for lack of trying. In the early 2000s I began teaching classes at the University of Toronto. One was titled "Software Architecture", and after three very frustrating offerings I told the department they should cancel it. The problem was that the half-dozen textbooks I read with "software architecture" in their titles spent hundreds of pages explaining how to elicit architectural requirements and how to document architectures, but devoted less than 20 pages in total to describing actual systems. Students memorized what I put in front of them and passed their exams, but it had no impact on how they thought or what they built.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.033 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".