Report of the IPBES Indigenous and local knowledge dialogue workshop on scenarios of the future, 23-26 May 2025, Subic Bay, Philippines. Bonn, IPBES
Bibliographic record
Abstract
This report summarises the proceedings of the Indigenous and local knowledge (ILK) dialogue workshop on scenarios of the future that was organized by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystems Services (IPBES). The full title of the workshop agreed by the IPBES Plenary was “Workshop to reflect on scenarios and models to better account for different knowledge systems, including Indigenous and local knowledge systems, and Mother Earth-centric scenarios and models.”The dialogue workshop was held in Subic Bay, the Philippines, from 23 to 26 May 2025. It aimed to provide a platform for discussion between Indigenous Peoples and local communities and members of the IPBES task forces on Indigenous and local knowledge and on scenarios and models, as well as others with experience and expertise in scenarios work. This report aims to provide a written record of the dialogue workshop, which can inform the future work of the IPBES task forces and can be a resource for all dialogue participants who may wish to review and contribute to the IPBES work at the interface of scenarios and models and Indigenous and local knowledge, as well as others who may be interested in this theme. The report is not intended to be comprehensive or to provide definitive resolution to the many engaging discussions that emerged during the workshop. Rather, it serves as a written record of those discussions, which will continue to develop and evolve in the months and years ahead. For this reason, clear points of agreement are discussed, but also, if there were diverging views among participants, these are also presented for further attention and discussion
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".