Bibliographic record
Abstract
This dataset consists of archived copies of the case data file from <a href="http://participedia.net/">Participedia Project</a> and its codebook. These archived copies will be periodically updated. To access the most up-to-date version of the Participedia case data file and codebook, go to <a href="http://participedia.net/en/browse/cases">the Participedia "Cases" tab</a>, click on the ‘↓↑CSV’ button, and follow the screen instructions. <a href="http://participedia.net/">Participedia</a> aims to harness the power of collaboration to allow access to a growing source of comparable qualitative and quantitative on cases of new forms of participatory politics and governance around the world. At <a href="http://participedia.net/">Participedia</a>, registered users can upload details of cases by using the ‘create content’ form. The data in this case data file are crowd-sourced as details of cases are uploaded to the Participedia site by users. Therefore, the numbers of observations grow over time and the data for cases can change as they are edited. In the case data file, there is only one row per observation (case), with the exception of translated cases which can be identified using the TranslationNodeID (column C in the dataset). For more information, see the codebook below.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.168 |
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".