Inter-Rater Agreement, Data Reliability, and The Crisis of Confidence in Psychological Research
Bibliographic record
Abstract
In response to the crisis of confidence in psychology, a plethora of solutions have been proposed to improve the way research is conducted (e.g., increasing statistical power, focusing on confidence intervals, enhancing the disclosure of methods). One area that has received little attention is the reliability of data. We note that while it is well understood that reliability of measures is essential to replicability, there is a failure to apply some measure of data reliability consistently, or to correct for chance when assessing agreement. We discuss the problem of relying on Percent Agreement between observers as a measure of reliability and describe a dilemma that researchers encounter when assessing contradictory indicators of reliability. We conclude with some pedagogical strategies that might make the need for reliability measures and chance correction more likely to be understood and implemented. By so doing, researchers can contribute to solving some aspects of the crisis of confidence in psychological research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.839 | 0.924 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".