Fundamental Flaws in the Design and Reporting of Chew and Neo (2024)
Bibliographic record
Abstract
Abstract Loot boxes are products inside video games that consumers can buy to obtain random rewards. They are prevalently implemented in contemporary video games, including in those deemed suitable for young children. Stakeholders (parents and policymakers) are concerned about their gambling-like nature and potential harms. An established line of research has found positive correlations between loot box spending and problem gambling and problem video gaming that justify stricter regulation. Chew and Neo (2024) also sought to explore these relationships and presented findings that were contrary to the prior literature. In principle, challenging our current knowledge using novel methods can improve the overall reliability of science and should always be encouraged. However, those methods must be sound. Unfortunately, Chew and Neo (2024) was fundamentally flawed due to a major error in its survey materials: in relation to the most important variable, they incorrectly instructed participants that the highly popular video game League of Legends did not contain loot boxes, which was factually incorrect, as the game did sell loot boxes. This significantly affected the accuracy of the data and the subsequent results and interpretation. Besides this fundamental error, the study also suffers from several other critical shortcomings that call its validity into question, including (i) measuring and relying upon an unreliable variable, (ii) potentially unjustified exclusion of participants, and (iii) the misuse of statistics. More proactive engagement with open science practices would have alleviated our concerns or even prevented these issues from arising in the first place. Our analyses suggest that the validity of the results in Chew and Neo (2024) may have been compromised and should be interpreted with caution for meta-analysis and policymaking purposes.
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 | 0.677 | 0.866 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".