Bibliographic record
Abstract
The systematic literature review (SLR) increases the grounding of new research through the disciplined incorporation of existing research. As an information systems (IS) discipline, it would seem necessary and productive to structure our practices in such a clear and explicit way through SLR. In doing so, we would emulate the methods used in other respectable disciplines such as medicine and other evidence-based fields. Furthermore, our student disciples to the discipline would be able to follow a codified process in conducting literature reviews. Having a clearly delineated literature review process that is repeatable, unbiased, accountable, and transparent appears beyond critique.
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 | 0.657 | 0.830 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.016 | 0.103 |
| Scholarly communication | 0.037 | 0.033 |
| Open science | 0.018 | 0.037 |
| Research integrity | 0.133 | 0.131 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".