Preventing and Protecting Against Internet Research Fraud in Anonymous Web-Based Research: Protocol for the Development and Implementation of an Anonymous Web-Based Data Integrity Plan
Bibliographic record
Abstract
BACKGROUND: Data integrity is a priority in any internet research study; it should be maintained to protect the safety and privacy of human participants and to maintain the validity and reliability of research findings. However, one noteworthy risk of web-based research is fraudulent respondent activity. When investigators must utilize anonymous web-based recruitment techniques to reach hidden and expanded populations, steps should be taken to safeguard the integrity of data collected. OBJECTIVE: The purpose of this paper is to present a novel protocol in the form of an anonymous web-based research data integrity plan (DIP) protocol that outlines steps for securing data integrity while conducting anonymous web-based data collection. METHODS: In this paper, we discuss a protocol regarding the development and implementation of a specific DIP in response to fraudulent activity in an original large-scale mixed methods study launched in April 2021. Four primary steps, each with a set of affiliated procedures, are presented: (1) defining the risks, (2) planning research protocols, (3) securing data collection and recruitment, and (4) determining enrollment. RESULTS: Following the relaunch of a large-scale original study and implementation of the DIP protocol, preliminary analyses demonstrated no fraudulent activity. A pre-post analysis is underway to evaluate the effectiveness of the DIP strategies from February 2022 through May 2023. CONCLUSIONS: Implementing the DIP protocol could save valuable research time, provides a process to examine data critically, and enables the contribution of rigorous findings to various health fields. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38550.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.105 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".