Recruitment Strategies Bias Sampling and Shape Replicability
Bibliographic record
Abstract
Replicating psychological research has become a central concern for psychologists. Although attention has been paid to the possibility of heterogeneous populations driving replication success/failure, the heterogeneous recruitment strategies researchers use to draw samples from those populations are often overlooked. Yet recruitment strategies may bias the participants who show up and shape replication results. We examine this idea through several unique paradigms (sampling North American university students, N total = 1,009). First, subtle manipulations of recruitment strategies (i.e., mentioning cash, expedient credit, fun, or a study narrative) were differentially appealing to individuals varying on experiential versus reward-based motivations (Experiment 1). Second, employing different recruitment strategies biased the motivational styles of actual participant show-ups, and sometimes even shaped the success of several replication studies (Experiment 2–3). We conclude that recruitment strategies may sometimes alter the degree of successful replication.
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 | 0.574 | 0.732 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".