Bibliographic record
Abstract
As introduced in Chapter 1, in this thesis, I developed guidelines to research institutions on how to foster research integrity. I did this by exploring how research institutions can develop policies to foster, and raise awareness about, research integrity. In Section 1 of the thesis, my goal was to set the research agenda by investigating current practices of research integrity promotion at research institutions (the descriptive step), and exploring which topics should be addressed in institutional research integrity policies (the normative step). I addressed the descriptive step of looking at current practices, through a scoping review (Chapter 2). In this chapter, I found that while there are already many institutional practices for research integrity promotion globally, most of them focus on researchers’, rather than institutions’ responsibilities for fostering research integrity. In Chapter 3, I tackled the normative question of which topics should be included in institutional research integrity policies. Using a Delphi study that included a number of research policy experts and research leaders, I developed a comprehensive list of 12 topics that research institutions should address to foster research integrity. Section 2 of the thesis focused on developing guidelines for research institutions on research integrity. I specifically zoomed in further into ‘Research integrity education and training’ as the topic of the guidelines here. The first step to developing the guidelines was to examine researchers’ and other research stakeholders’ views and preferences regarding how research institutions can develop and implement better research integrity education and training policies (Chapter 4). Using focus groups, I found that researchers and other research stakeholders support the provision of continuous research integrity education which targets all researchers (across ranks), and other institutional stakeholders (such as research integrity officers and institutional leaders). Next, I proceeded to co-creating institutional guidelines on research integrity education and training together with users. In Chapter 5, I discussed how research integrity guidelines can be jointly developed with users using co-creation methods – methods engaging participants in interactive exercises aimed at jointly developing user centered outputs. The resulting RI education and training guidelines are presented in detail Chapter 6. The guidelines address the research integrity education of a) bachelor, master and PhD students; b) post-doctorate and senior researchers; c) other research integrity stakeholders; as well as d) continuous research integrity education. In the guidelines, I recommend the implementation of mandatory research integrity training (for all academic ranks); follow-up refresher training; informal discussions about research integrity; appropriate rewards and incentives for active participation in research education; and evaluation of research integrity educational events across target groups. In Section 3 of this dissertation, I reflected on an implementation concern regarding the guidelines developed. I explored the question of how research institutions can combine the implementation of research integrity rules with fostering researchers’ commitment to engage in responsible research practices (Chapter 7). I argued that institutions can use and combine market (governance through incentives), bureaucracy (governance through rules) and network (cooperative governance) mechanisms to foster research integrity. Using Habermas’ Theory of Communicative Action, I discussed that institutions can use bureaucratic and market mechanisms to foster research integrity (such as rules and incentives, respectively), as long as these are rooted in network processes (e.g. involvement of stakeholders in the development and improvement of rules or incentives). In Chapter 8, I concluded that: 1) the framing of research integrity matters for institutional policies; 2) research integrity guidelines should be tailored to the local context at hand; 3) it is important to be aware of and countervail the danger of creating a box-checking mentality when implementing institutional research integrity policies; and 4) research integrity is a journey.
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 | 0.310 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.032 | 0.043 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.018 | 0.025 |
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".