A Perspective on the Relevance and Public Reception of Psychological Science
Bibliographic record
Abstract
In this short commentary, data from the website Reddit is used to examine how people receive social psychological research. The data show that people care greatly about research dealing with humans: links tagged as psychology, social sciences, and health are upvoted more than other categories on Reddit. Within the category of psychology, articles were coded based on the topic of research. Articles dealing generally with social psychological topics are among the highest in number and upvotes on the subreddit r/Science. Many posts were upvoted tens of thousands of times. However, upvotes on Reddit are unrelated to scientific publishing metrics (e.g., impact factor, journal rankings, and citations), suggesting a disconnect between what psychologists and Redditors may see as relevant. These findings also highlight some points for reflection. For example, psychologists may benefit from thinking about the purpose, goals, and beneficiaries of the research they pursue. Additionally, the level of attention that some psychological research receives has implications for transparent research practices. Researchers have a responsibility to ensure that findings are reported accurately and transparently because, whether scientists like it or not, people care about psychological research, they share it, and use it in their lives.
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 | 0.043 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".