Argentina expanding Climate Services Knowledge Frontiers: capacity building for local stakeholders.
Bibliographic record
Abstract
The construction of local capacities related to climate change adaptation, such as climate indicators, is crucial to generate effective strategies against climate hazards. Along these lines, a "Workshop on Co-creation of Climate Indicators" has been conducted in the municipality of Posadas (Misiones, Argentina), adapting the co-creation methodology developed by Font et al. (2021). The purpose was to develop local capacity building, including the provision to participants with WMO competencies for delivering Climate Services. To achieve this, participants acquired skills and abilities that help them think, discuss, define, and compute climate indices to assist decision-making in climate-dependent sectors.The workshop was carried out in collaboration with a local partner, REDAPPE, enabling a comprehensive diagnosis of the territory and all stakeholders involved. Local work and outreach facilitated the participation of stakeholders from the private, public, and civil society sectors. Furthermore, both the provincial government (Misiones Province) and the local government (Posadas municipality) engaged with the process, actively participating and providing support.During the workshop, co-created indicators were generated in different working groups. Additionally, each participant conducted individual work where they explained why they consider the development of such capacities important and how they would apply them in their work. Finally, networks of collaboration and work have been established among participants, facilitating the exchange of knowledge and information among them during and after the workshop. We understand this as a fundamental strength for the construction of effective and coordinated actions in the territory; therefore, part of the process is to ensure the support of the technical team with the participants and their work, as well as the promotion and facilitation of tools for the development of new projects.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".