A decolonized science requires bigger, bolder, and less incremental change: Commentary on Sharpe (2024).
Bibliographic record
Abstract
This commentary is written in response to Sharpe's (2024) article titled "Editor Bias and Transparency in Psychology's Open Science Era." The article clearly describes the conversation on bias, transparency, and editor accountability occurring in the field of psychology in recent years. However, in this era of public accountability, where there is a groundswell seeking a more decolonized science, we use the commentary to discuss how the article could have gone further. We used an equity model to explore whether the model of change being proposed by Sharpe is at the right level of analysis and whether it is equipped with the needed ingredients to bring about a solution to the long-standing problem of editor bias and lack of transparency. We offer an alternative to the individual model that Sharpe's article puts forth and recommend the use of a systems thinking approach to generate action items for a more decolonized science in the realm of publishing and editor bias. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.028 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.046 | 0.052 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".