Bibliographic record
Abstract
The ability to design studies that are free from confounded variables is an acquired skill that separates the true psychological scientist from a layperson. The latter might be quite capable of generating interesting questions that could be addressed by psychological research; turning these questions into a study that cleanly tests the hypotheses, however, can be quite a challenge. It is this topic that the present chapter addresses. Consider the fundamental goal of psychological research: to discover the causes and consequences of behavior. The only way to make such discoveries is to be able to examine data from a study that is free from alternative explanations. Such alternative explanations most often arise when an experiment contains confounded variables . Confounded variables involve the “simultaneous variation of a second variable with an independent variable of interest so that any effect on the dependent variable cannot be attributed with certainty to the independent variable” (Elmes, Kantowitz, & Roediger, 2003, p. 436). A well-designed study is one in which the researcher has carefully considered potential alternative explanations and designed the study so that these alternative explanations are no longer viable. Psychological research can be categorized into two broad classes: experimental studies and correlational studies. In the former case, the researcher manipulates the variable of interest (the independent variable) and observes its effects on the dependent variable. The latter involves examining variation that occurs naturally (e.g., the variation between emotional awareness and happiness) and attempts to draw conclusions regarding this relationship.
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 | 0.339 | 0.527 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".