Bibliographic record
Abstract
Despite being one of the most well-known laboratory-based tasks in psychology, certain methodological and theoretical considerations surrounding the marshmallow test have gone largely unstudied until recently. These considerations reflect gaps in the delay of gratification literature and broader contemporary issues concerning the replicability of seminal findings and the lack of an agreed upon theoretical framework in the field. Accordingly, my dissertation uses the classic marshmallow test to explore the contemporary issues of replication and theory in psychology in a series of three studies. In Study 1, the marshmallow test is at the center of a case study unpacking the nuances of direct and conceptual replication; a tool designed to support ongoing replication efforts is proposed. Study 2 executes a full-scale replication of the paradigm from the case study, and introduces a methodological extension to improve the paradigm’s experimental rigour while making it amenable to an evolutionary–developmental framework. Finally, Study 3 applies an evolutionary–developmental framework to examine how this perspective might help account for individual differences in marshmallow test behavior. Through these three studies, my dissertation provides an example of how engaging in replication and applying an evolutionary–developmental framework to the marshmallow test literature to inform outstanding theoretical questions in psychology might be mutually beneficial endeavors.
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 | 0.069 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".