Frightened by an Old Scarecrow: The Remarkable Resilience of Demand Characteristics
Bibliographic record
Abstract
More than 50 years ago, the idea of demand characteristics was introduced by Martin Orne in a widely cited American Psychologist article. Through the 1960s and the mid-1970s, numerous studies were conducted investigating the role of demand characteristics in a variety of research areas. Demand characteristics faded from researchers’ attention in the late 1970s, relegated to brief descriptions in research methods textbooks. The present article traces the origins of and battles fought over demand characteristics during its heyday. Evidence is provided that suggests demand characteristics experienced a rebirth in the 1980s and it remains a widely referenced idea up to today. Demand characteristics reflect perennial concerns about the difficulties of and limitations to doing research with humans, concerns that often surface in the periodic crises that confront psychology. The types of problems that animated the crisis of confidence associated with demand characteristics in the 1970s form one dimension of the current replication crisis. Reinterpretation of this current replication crisis and a new direction for experimental research with human subjects are derived from this review of demand characteristics.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".