Deliverable D2.8 Revised use-cases for the FWCR Platform version 2.0 (PAR Cycle 2)
Bibliographic record
Abstract
The TURNkey concept model is being developed over three cycles of participatory action research (PAR). This report describes the process and findings of the 2nd PAR cycle in the TURNkey project. In light of these findings, the report reviews the end-user use cases that were developed for TURNkey during the 1st PAR cycle (which have been reported in D2.6) and revises them in light of the discussions with end-users and TURNkey scientists, engineers and software developers that occurred during the 2nd PAR cycle. The report lays the foundation for the 3rd and final round of PAR that will be conducted for TURNkey. It will also inform TURNkey deliverable D7.7, which will provide end-users with an (exemplar) model Business Continuity and Resilience Plan (BCRP) and Disaster Management Plan (DMP) framework for integrating the TURNkey FWCR platform into their disaster management planning process. This report provides the following: Review of the key lessons from the 1st PAR Cycle Online workshops with potential end-users using a virtual demonstrator (process and findings) SWOT analysis with TURNkey scientists and engineers (process and findings) TURNkey application workshop around a hypothetical hospital scenario (process and findings) Consortium-wide reflection on findings from the 2nd PAR Cycle (process and findings) Revised end-user use cases Revised table of TURNkey features, what end-users want vs what is possible and in scope A revised version of the FWCR concept model A conclusion and next steps
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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.054 |
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".