Raising the value of research studies in psychological science by increasing the credibility of research reports: The Transparent Psi Project - Preprint
Bibliographic record
Abstract
The low reproducibility rate in social sciences has produced hesitation among researchers in accepting published findings at their face value. Despite the advent of initiatives to increase transparency in research reporting, the field is still lacking tools to verify the credibility of research reports. In the present paper, we describe methodologies that let researchers craft highly credible research and allow their peers to verify this credibility. We demonstrate the application of these methods in a multi-lab replication of Bem’s Experiment 1 (1) on extrasensory perception (ESP), which was co-designed by a consensus panel including both proponents and opponents of Bem’s original hypothesis. In the study we applied direct data deposition in combination with born-open data and real-time research reports to extend transparency to protocol delivery and data collection. We also used piloting, checklists, laboratory logs and video documented trial sessions to ascertain as-intended protocol delivery, and external research auditors to monitor research integrity. We found 49.89% successful guesses, while Bem reported 53.07% success rate, with the chance level being 50%. Thus, Bem’s findings were not replicated in our study. In the paper we discuss the implementation, feasibility, and perceived usefulness of the credibility-enhancing methodologies used throughout the project. Plain word summary:This project aimed to demonstrate the use of research methods designed to improve the reliability of scientific findings in psychological science. Using this rigorous methodology, we could not replicate the positive findings of Bem’s 2011 Experiment 1. This finding does not confirm, nor contradict the existence of ESP in general, and this was not the point of our study. Instead, the results tell us that (1) the original experiment was likely affected by methodological flaws or it was a chance finding, and (2) the paradigm used in the original study is probably not useful for detecting ESP effects if they exist. The methodological innovations implemented in this study enable the readers to trust and verify our results which is an important step forward in achieving trustworthy science.
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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 | 0.474 | 0.597 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".