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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".