(Re)considering the Concept of Literature Review Reproducibility
Bibliographic record
Abstract
Literature reviews play a key role in academic research by describing, understanding, explaining, and testing the constructs and theories within a particular topic area. In recent years, various commentaries, debates, and editorials in the information systems (IS) field’s top journals have highlighted the importance of a trustworthy literature review process, including detailed discussions on systematicity and transparency. Although the reproducibility of a literature review has also been noted as important, it remains less recognized because of several terminology-related issues. This ambiguity could result in misunderstandings regarding the degree of trust that should be placed in a literature review’s process. In this research essay, we seek to clarify what makes a literature review reproducible, how it is distinct from related concepts, and when achieving it is desirable and feasible. We propose a series of clarifications and remedies to assist scholars within and outside the IS field in the preparation of stand-alone reviews.
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 | 0.667 | 0.904 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.026 | 0.045 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".