Bibliographic record
Abstract
Time flies. A five-year tenure as editor-in-chief of the Communications of the Association for Information Systems (CAIS) comes to an end in June, 2020. When I started that position, I had just become a father for the first time. Now, I have two young boys and a third baby on the way. With this editorial, I look back at my time with a journal that I have always been a fan of. CAIS has a great tradition of publishing papers that shape the discipline. When I started, I wanted to ensure this tradition continued. I wanted to see CAIS maintain its important role as the key communications outlet of the Association for Information Systems: I wanted to see it preserve its standing as a traditional, broad-range journal that can be a home for many different types of content worth communicating: research, panels, commentaries, tutorials, pedagogy, and so forth. I also wanted to make sure that the global IS community appreciates the journal’s mission and operations. As I step down from my role, I reflect on the CAIS community’s efforts toward these goals in this brief commentary.
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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 | 0.028 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.025 | 0.038 |
| Insufficient payload (model declined to judge) | 0.015 | 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".