Senior Scholars Panel: What Do We Like About the IS Field?
Bibliographic record
Abstract
Some of us have been in the information systems field for a long time. What do we like about the field? (Grover et al. 2009). We think the field of information systems is distinctive, perhaps with respect to subject, methods, and a certain way of thinking (Baskerville and Myers 2002; Benbasat and Zmud 2003; Sidorova et al. 2008). Assuming we are not simply drowned in cognitive dissonance, there are important reasons for us to believe in this field and for us to hope that it prospers. We might or might not have a clear and common message about the distinctive nature of the field, but we can at least get some views from some senior scholars who are both smart enough to have jumped ship if they had wanted, and committed enough to see it through. We invite them to present their views, and then we invite the audience to engage in a discussion about the field. Perhaps we might even come up with some clear and common things to say about the field?
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.019 | 0.022 |
| 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".