Don't Throw the Baby Out With the Bathwater: Comparing Data Quality of Crowdsourcing, Online Panels, and Student Samples
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
In their focal article, Landers and Behrend (2015) propose to reevaluate the legitimacy of using the so-called convenience samples (e.g., crowdsourcing, online panels, and student samples) as compared with traditional organizational samples in industrial–organizational (I-O) psychology research. They suggest that such sampling strategies should not be judged as inappropriate per se but that decisions to accept or reject such samples must be empirically or theoretically justified. I concur with Landers and Behrend's call for a more nuanced view on convenience samples. More precisely, I suggest that we should not “throw the baby out with the bathwater” but rather carefully and empirically examine the advantages and risks associated with using each sampling strategy before classifying it as suitable or not.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it