ICIS 2017 Panel Report: Break Your Shackles! Emancipating Information Systems from the Tyranny of Peer Review
Bibliographic record
Abstract
The paper presents the report of a panel that debated the review process in the information systems (IS) discipline at ICIS 2017 in Seoul, Korea. The panel asked the fundamental question of whether we need to rethink the way we review papers in the discipline. The panelists partnered with the audience to explore some reviewing limitations in IS today and the ways that reviewing in the discipline might change to address some of its difficulties. We first report key concerns with modern reviewing. We then present arguments for and against three proposals (i.e., paying for reviews, mandatory reviews, and open reviews) and a panel audience vote on the issues. We neither advocate for nor condemn these solutions but rather use them to illustrate what we believe represent the core underlying issues with reviewing in the IS discipline. Specifically, we believe the key stumbling blocks to effectively improving our review process include 1) a lack of empirical data on actual practice, 2) a lack of clear goals, and 3) an ignorance of the possible solutions to the review dilemma that the wider literature articulates.
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 | 0.162 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.044 | 0.037 |
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".