Evaluating Reddit as a Crowdsourcing Platform for Psychology Research Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Online crowdsourcing platforms, such as Amazon Mechanical Turk (MTurk), have become popular alternatives to the ubiquitous student samples used in psychology research. r/SampleSize, an alternative pool on the website Reddit, allows for online participant recruitment without compulsory or immediate payment, making it potentially useful for students, research trainees, and course instructors. Objective: The current study sought to assess the viability of using r/SampleSize as a participant pool by comparing its data characteristics to MTurk and existing lab samples. Method: Two hundred and fifty-six MTurk workers and 277 r/SampleSize participants completed identical questionnaires on demographics, participation motivations, and standard psychology scales. Results: Participants recruited through r/SampleSize reported diverse ages, education levels, income, and employment, although White ethnic background and US residence were predominant. r/SampleSize participants were more internally motivated than MTurk to participate in research and had greater need for cognition but did not differ significantly in altruism or motivation to gain self-knowledge. r/SampleSize data reliability and quality were comparable to MTurk and lab samples across most analyses. Teaching Implications: r/SampleSize can be used to recruit relatively large and diverse samples for undergraduate research projects with minimal setup, labor, and cost. Conclusion: The findings suggest that r/SampleSize is a diverse and viable participant pool.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it