Ensuring durability of community-university engagement in a challenging context: Empirical evidence on Science Shops
Bibliographic record
Abstract
Universities’ community engagement is confronted with growing pressure from increased competition and marketisation of knowledge, along with widespread adoption of New Public Management measures. This context is notably challenging for forms of engagement that are based on such principles and practices as cooperation, knowledge democracy and public value. Within this framework, this article identifies competencies and strategies that may ensure durability of community-university partnerships. The article presents the results of two different, yet coherently connected, research endeavours on Science Shops in Europe. Science Shops are a unique way to organise relationships between science and society mainly by responding to research questions arising from citizens and/or Civil Society Organisations (CSOs), usually by means of a participatory methodology and active involvement of students. Empirical evidence for this article was gathered by means of a wide range of different techniques, such as structured questionnaires, focus groups, interviews, direct observation and document analysis. In the first research effort, a questionnaire was delivered to European Science Shops in order to produce mainly descriptive statistics prior to progressing to case studies and focus groups which would generate more in-depth knowledge and understanding. The second study program was connected to formative and summative evaluation of a European Commission funded project aimed at embedding Responsible Research and Innovation (RRI) in Higher Education curricula through Science Shops (namely EnRRICH – Enhancing Responsible Research and Innovation through Curricula in Higher education). Participatory evaluation was carried out mainly on pilot projects run by project partners. Results are discussed in the light of relevant literature regarding possible strategic assets that may enable Science Shops and Community Engagement units to overcome observed fragility and ensure durability. This can be pursued through systematic mobilisation of specific knowledge, competencies and abilities. Combinatory capacity and boundary spanning are pinpointed as specific components of Science Shops’ action, which – we maintain – are also key strategic assets to consolidate their role and ensure durability. The distinction between the ‘instrumental/operational’ and ‘strategic’ function of boundary spanning is introduced in order to analytically develop this argument.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.077 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".