Scientists' commitment to underperforming research projects: linking past success and the social environment
Bibliographic record
Abstract
This article investigates scientists' commitment to underperforming research projects based on the concomitant consideration of their past success and social environments. Based on escalation of commitment and network theory, the model hypothesizes that past success triggers the commitment to underperforming projects but that the strength of this influence varies depending on the characteristics of decision makers' social networks. Results from the analysis of 3,072 scenario assessments nested within 96 scientists show that the positive relationship between past success and continued investment in underperforming projects is more positive when the network is larger, when the ties within the network are stronger, and when feedback from network partners is predominantly positive. Surprisingly and contrary to model predictions, results also show that the relationship between past success and scientists' tendency to commit to underperforming projects becomes stronger with lower communication frequency with network partners. This study extends current research by exploring the boundary conditions of the impact of decision makers' social environment on commitment to failing projects. Further, it adds to literature on the downside of success by emphasizing that decision makers, particularly those in some social environments, are driven to commit additional resources to underperforming – and potentially failing – projects. Decision makers acting in such environments should be aware that they are prone to overinvestment of resources, and the findings of this study can help them increase their awareness. Based on this study's results, decision makers (including scientists) can thus better reflect on and improve their research project evaluations. Finally, the findings of this study open up various opportunities for future research.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".