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Enregistrement W4313555477 · doi:10.5463/thesis.19

Developing guidelines for research institutions

2023· dissertation· en· W4313555477 sur OpenAlexfundno aff
Krishma Labib

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineDecision Sciences
Thématiquescientometrics and bibliometrics research
Établissements canadiensnon disponible
Organismes subventionnairesCordisWomen's College Research InstituteEuropean Commission
Mots-clésPromotion (chess)NormativePolitical sciencePublic relationsResearch integrityEngineering ethicsDelphi methodKnowledge managementEngineeringComputer sciencePolitics

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,310
score de la tête « metaresearch » (Gemma)0,484
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Bibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,987
Score d'incertitude au seuil0,851

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,3100,484
Méta-épidémiologie (sens strict)0,0020,004
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0130,012
Études des sciences et des technologies0,0130,016
Communication savante0,0320,043
Science ouverte0,0110,023
Intégrité de la recherche0,0260,028
Charge utile insuffisante (le modèle a refusé de juger)0,0180,025

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,986
Tête enseignante GPT0,810
Écart entre enseignants0,175 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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