Research Standards for Promotion and Tenure in Information Systems1
Bibliographic record
Abstract
What constitutes excellence in information systems research for promotion and tenure? This is a question that is regularly addressed by members of promotion and tenure committees and those called upon to write external letters. While there are many elements to this question, one major element is the quality and quantity of an individual’s research publications. An informal survey of senior Information Systems faculty members at 49 leading U.S. and Canadian universities found 86 percent to expect three or more articles in elite journals. In contrast, an analysis of publication performance of Ph.D. graduates between the years of 1992 and 2004 found that approximately three individuals in each graduating year of Ph.D.s (about 2 percent) published 3 or more articles in a set of 20 elite journals within 6 years of graduation. Only 15 individuals from each graduating year (11 percent) published one or more articles. As a discipline, we publish elite journal articles at a lower rate than Accounting, yet our promotion and tenure standards are higher, similar to those of Management, Marketing, and Finance. Thus, there is a growing divergence between research performance and research standards within the Information Systems discipline. As such, unless we make major changes, these differences will perpetuate a vicious cycle of increasing faculty turnover, declining influence on university affairs, and lower research productivity. We believe that we must act now to create a new future, and offer recommendations that focus on the use of more appropriate standards for promotion and tenure and ways to increase the number of articles published.
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 | 0.425 | 0.548 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".