Guidelines for reporting research using systematic coding of observed human behaviour (SCOBe)
Bibliographic record
Abstract
Abstract Systematic coding of observed human behaviour (SCOBe) is used across disciplines and topics but methodological reporting is often incomplete. We developed internationally generated, interdisciplinary guidelines for methodological reporting of such research. Using Delphi methodology, a working group of 22 experts sought group consensus in three rounds. Participants first assessed an initial set of reporting criteria (round 1). Next, in interactive meetings participants revised these criteria and reached consensus on reporting content (rounds 2 & 3). We present 20 criteria constituting the first comprehensive reporting guidelines for SCOBe research using existing, newly developed, or modified coding systems. The criteria encompass three procedural domains: 1. Research context; 2. Properties of the coding scheme; and 3. Application of the coding scheme. The presented guidelines will assist in substantiating and assessing the quality of SCOBe research. We encourage researchers to adopt these guidelines, to enhance quality of mono- and interdisciplinary research.
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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 | 0.603 | 0.765 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.051 | 0.043 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".