Registered Replication Report
Bibliographic record
Abstract
Language can be viewed as a complex set of cues that shape people's mental representations of situations. For example, people think of behavior described using imperfective aspect (i.e., what a person was doing) as a dynamic, unfolding sequence of actions, whereas the same behavior described using perfective aspect (i.e., what a person did) is perceived as a completed whole. A recent study found that aspect can also influence how we think about a person's intentions (Hart & Albarracín, 2011). Participants judged actions described in imperfective as being more intentional (d between 0.67 and 0.77) and they imagined these actions in more detail (d = 0.73). The fact that this finding has implications for legal decision making, coupled with the absence of other direct replication attempts, motivated this registered replication report (RRR). Multiple laboratories carried out 12 direct replication studies, including one MTurk study. A meta-analysis of these studies provides a precise estimate of the size of this effect free from publication bias. This RRR did not find that grammatical aspect affects intentionality (d between 0 and -0.24) or imagery (d = -0.08). We discuss possible explanations for the discrepancy between these results and those of the original study.
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 | 0.062 | 0.373 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.239 | 0.114 |
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".