Bibliographic record
Abstract
This article presents the findings of an experimental study focusing on the effect of semantic priming on survey respondents. The study involved manipulation of survey questions so that one version included priming triggers and the other one did not. The two versions of the survey were tested on two groups of Canadians (50 respondents each). The results confirmed the authors’ hypotheses, as they demonstrated that the inclusion of the priming triggers activated the relevant concepts in respondents’ minds and, as a result, they included concepts similar to those triggers in their responses to open-ended questions. To be precise, respondents who were exposed to priming triggers “one”, “first” and “three”, as well as “saving on food”, were significantly more likely to say they shop one or three times a week and to recall the grocery store “Save-On-Foods” in an unaided recall qustion. The findings in this study have theoretical and empirical significance and should be taken into consideration by all the researchers who design questionnaires in their research projects. Based on this research, one can conclude that a researcher who designs questionnaires should be cautions and make sure to sequence questions in a way that would minimize the priming effect on questions following priming triggers.
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 | 0.017 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".