Computer-mediated communication in ALICE-RAP: A methodology to enhance the quality of large-scale transdisciplinary research
Notice bibliographique
Résumé
ABSTRACTThe solving of complex social problems often calls on the public sector to stimulate, support and coordinate multidisciplinary, multi-sector action programmes, including public research programmes. When actors from disparate backgrounds and viewpoints gather to formulate and implement solutions, achieving effective communication is a special challenge. Computer-mediated communication (CMC) is often helpful in this regard, but it is still under development. A CMC innovation addressed by this research project is how scientific methods can be used to analyse, interpret and feedback CMC data and results to management, to facilitate large-scale publically financed transnational and transdisciplinary research (TDR). In a qualitative study design, data were collected at the first research meeting of EU's Addictions and Lifestyles in Contemporary Europe - Reframing Addictions Project (ALICE RAP), in Barcelona, May 2011. The participants were 104 scientists with backgrounds in more than 40 disciplines/specialties from 73 research institutions in 31 countries. Three CMC discussions were conducted with the scientists working simultaneously in groups of approximately 10, used computers to post comments to TV monitors visible to all participants, on three subjects: how ALICE RAP should be managed, what its mission should be, and the scientists' diverse values and ideas regarding addiction research and policy. The CMC produced 510 posts, 212 on management, 146 on mission and 152 on values, analysed using content analysis. Participants discussed their disciplinary, language and cultural diversity, and the need to manage diversity to avoid problems. They raised the issue that ALICE RAP is not just TDR, it is also transcultural, and this adds another challenge to TDR. The discussion about values revealed a preference for reframing addictions so as to reduce stigmatization and marginalization. It is concluded that CMC is a viable way to facilitate dialogue about complex issues in the conduct of TDR on addictions, when large numbers of scientists from highly divergent backgrounds are involved. The findings from analyzing CMC data can be used by managers to fine tune functioning and collaboration in a very complex research network like ALICE RAP, as well as other types of public sector networks.Key words: transdisciplinary research, networks, addictions, computer-mediated communication, public sector managementIntroductionThe administration of public sector research has taken on new levels of complexity in recent decades, for several reasons. First, public research programmes have become ever more targeted on developing policy solutions to major social problems and the processes by which science influences policy formation are multifaceted (Pohl, 2007). Following from this, collaboration is becoming more the rule than the exception, as realisation grows that many of the most significant social challenges know no provincial, national or regional boundaries; this is starkly evident with regard to the interaction of human activities and climate change, and the interaction between globalisation and public health, to name two prominent examples. International teams are assembled to address such problems and the multi-cultural nature of such teams adds yet another dimension of complexity to research management (Brett, Behfar and Kern, 2006).Major examples of publically-administered research programmes of high complexity are the seven research Framework Programmes of the European Union, which have promoted trans-national research since 1952, with the first projects in operation in 1955 under the European Coal and Steel Community Treaty and continuing to this day under Framework Programme 7 (see Laredo, 1998 for the historical developments). In the United States, the National Institutes of Health has long had a collaborative approach in the establishment of research teams that link researchers across disciplines, institutions and States, and include international collaboration (Mabry, et al. …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».