IUKWC Open Network members’ survey: summary report
Bibliographic record
Abstract
The India-UK Water Centre (IUKWC) promotes cooperation and collaboration between the complementary priorities of NERC-MoES water security research. The Open Network members’ survey was conducted in January 2019 under the authority of the IUKWC Management Board. At the time the survey was conducted, the membership stood at just over 800. The IUKWC had by this time convened four workshops, two Grassroots Field Exposure Surveys (GFES), and one User Engagement Initiative (UEI), as well as having supported three Pump Priming projects, and thirteen research exchanges. The aim of the survey was to determine what, if any, outcomes or impacts, members had derived from their engagement with the IUKWC, and from participating in the Centre’s activities, over the three years during which the Centre had been active. The survey was rolled out through the IUKWC website (www.iukwc.org) and ran for three weeks (8th – 29th January 2019). It was available only to Open Network Members, and the results were anonymised to remove any bias in analysing the results. The results from the survey were partially included in the Project Highlight Report (IUKWC, 2019), with a plan to publish the full results at a later date. The following is a collation of all the results from this survey, and a summary of how the results were used.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.047 |
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".