Survey data: The Contribution of European Citizen Science Projects to the UN Sustainable Development Goals (SDGs)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We launched and distributed a survey across European citizen science networks in summer 2020 via the online survey tool https://www.soscisurvey.de/. We presented an English and a German version. The survey was developed in preparation for the Citizen Science SDG conference ‘Knowledge for Change: A Decade of Citizen Science (2020-2030) in Support of the SDGs’, organised by the Museum für Naturkunde Berlin (Germany) on 14-15 October 2020, as an official event of Germany’s 2020 EU Council presidency. The questionnaire was open from July, 29 until October 12, 2020. In total, we received 195 responses (see raw dataset). Due to a lack of consent to data storage (n = 2) or incomplete records (n = 68), 125 responses could ultimately be included in the analysis. All survey data were analysed through simple descriptive statistics in order to summarise and combine the collected information (see Excel sheet also containing data analyses). Quotes from open spaces were used to support the results presenting real and practical experiences/concerns from the respondents. These answers were examined through simple content analysis. To do this, they were also categorised and quantified. The dataset includes: -raw data (cvs, N=195) -Excel-sheet with evaluations (n=125) -list of variables -list of values -questionnaire
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 it