Research Assessments Should Recognize Responsible Research Practices. Narrative Review of a Lively Debate and Promising Developments
Bibliographic record
Abstract
Abstract Research assessments have been under growing scrutiny in the past few years. The way in which researchers are assessed has a tangible impact on decisions and practices in research. Yet, there is an emerging understanding that research assessments as they currently stand might hamper the quality and the integrity of research. In this chapter, we provide a narrative review of the shortcomings of current research assessments and showcase innovative actions that aim to address these. To discuss these shortcomings and actions, we target five different dimensions of research assessment. First, we discuss the content of research assessment, thereby introducing the common indicators used to assess researchers and the way these indicators are being used. Second, we address the procedure of research assessments, describing the resources needed for assessing researchers in an ever-growing research system. Third, we describe the crucial role of assessors in improving research assessments. Fourth, we present the broader environments in which researchers work, explaining that omnipresent competition and employment insecurity also need to be toned down substantially to foster high quality and high integrity research. Finally, we describe the challenge of coordinating individual actions to ensure that the problems of research assessments are addressed tangibly and sustainably.
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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 | 0.158 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".