An editorial perspective on judging the quality of inductive research when the methodological straightjacket is loosened
Bibliographic record
Abstract
As inductive research has moved from the fringe to the mainstream, it not only has come to look more like deductive research, but has started to look more formulaic as well (i.e. standards, templates, checklists). The very thing that makes inductive research unique is its ability to challenge what is known and to do so creatively. The question, thus, needs to be asked: why does inductive research continue to become more formulaic when many inductive editors, reviewers, and authors celebrate novelty and creativity? We believe it is because reviewers and editors find it difficult to judge “quality” when there is no guidebook. The quality of science-based research is easier to judge than creative inductive research, which is often assumed to be in the “eye of the beholder.” From our SO!apbox, we tackle this challenge head-on by asking: what is “quality inductive research” when we loosen the science-based methodological straightjacket so as to deliver the novelty and creativity promised by inductive methods? In this editorial, we explore how editors can judge quality inductive research and offer innovative editorial practices that can help to foster creative inductive research.
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 | 0.099 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".