Living Lab as an approach to island rejuvenation through co-creation and Local Ecological Knowledge inclusion in the context of the Aegean Islet Conservation Program: the islet of Anthropofas as a case study
Bibliographic record
Abstract
The Aegean Islet Conservation Program is a research project focused on creating a protocol for islet restoration and solutions to land degradation in the Mediterranean, developed by the Archipelagos Institute of Marine Conservation and the John H. Daniels Faculty of Architecture, Landscape, and Design of the University of Toronto. Its first case study is constituted by the islet of Anthropofas, part of the Fournoi-Korseon archipelago. Within this initiative, this investigation develops a framework to integrate Local Ecological Knowledge (LEK) to scientific expertise, with the intention of enhancing collaboration and sense of ownership among different stakeholders throughout the entire project life cycle. The main proposition is to deploy a Living Lab user-centred approach to amplify collaboration among different actors and promote co-creation of both knowledge and solutions to existing challenges in the context of Anthropofas. Given the operational nature of this work, after the methodological core of this paper is explained, this is then applied to the program itself. As Participatory Methods are seen as a means to translate Living Lab theory into practice, current research being developed under the Aegean Islet Conservation Program is combined with a series of such tools, categorized according to the four main phases that constitute the development of Living Lab. This is followed by a reflection concerning the concrete potential for a co-creative process to unfold within the program, based on the way the project has been articulated so far by its proponents. The contribution concludes by discussing what limitations could affect the feasibility of the Living Lab-based framework for LEK inclusion and co-creation throughout the project, while also pointing out where future research pertinent to the program ought to head.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".