Bibliographic record
Abstract
Status quo bias (SQB) refers to the inclination of individuals to prefer and maintain the current state in decision-making. This study explores the profound impact of SQB on decision-making in the fields of medicine, technology, and business. Through an extensive review of existing literature, we examine how SQB significantly influences the decision-making process and its subsequent outcomes, hampering the progress of innovation and hindering the adoption of optimal choices. Rooted in factors such as familiarity, comfort, and resistance to change, SQB acts as a formidable barrier to embracing novel alternatives. Additionally, limited understanding and low market penetration further reinforce this cognitive bias. By gaining a comprehensive understanding of the nature and implications of SQB, policymakers and decision-makers can access valuable insights to guide future planning and strategies effectively, mitigating the adverse effects of this cognitive bias. The study aims to encourage progress in medicine, technology, and business domains by promoting the exploration and adoption of new products and practices. By acknowledging and addressing SQB, stakeholders can foster a culture of adaptability and open-mindedness, ultimately driving positive advancements in their respective fields.
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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 | 0.105 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".