HYDROUSA Social Impact Assessment results
Bibliographic record
Abstract
This dataset is part of HYDROUSA Deliverable 6.2 and include the results of the questionnaire introduced to 213 stakeholders. The scope of the Deliverable is to serve as an insightful guide for the HYDROUSA project's commitment to circular resource management. By applying an integrated approach, leveraging the Theory of Change methodology, with user-data evaluation, this report presents the narration of how innovative and circular technologies can serve societal benefits. As the findings emerge through communities, the potential for positive impact becomes evident, aligning technical ingenuity with social advancement.Through questionnaires and interviews we quantify inputs, outputs, and outcomes, this evaluation captures the immediate effects but also the potential long-term intentions of change. This data-driven analysis was applied through 213 inputs from stakeholders to support us to make informed decisions for the exploitation of the solutions and the optimization of these systems in terms of performance and required outputs. Furthermore, the analysed results identified through this evaluation provide actionable insights for maximizing the potential of successful implementation with positive impact for the community. By assessing public acceptance and willingness to apply circular approaches for water management, the report empowers users to align their strategies with community preferences, fostering a sense of ownership and collaboration.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".