Computer-mediated communication in ALICE-RAP: A methodology to enhance the quality of large-scale transdisciplinary research
Bibliographic record
Abstract
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. …
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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 | 0.151 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".