Action planning to improve issues of effectiveness, representation and scale in public participation: A conference report
Bibliographic record
Abstract
This conference report examines issues of effectiveness, representation and scale in deliberative processes by reporting on outcomes of the Participatory Approaches in Science and Technology (PATH) conference. The H-form and action planning (HAP) approach was used to guide 120 participating experts in a plenary workshop as they assessed the current state of practice and developed action plans for improving public participation in decision-making related to science and technology. The workshop outcomes highlighted the need for greater institutionalisation of participatory processes within decision-making structures and wider society, coupled with improved transparency in decision-making and increased emphasis on participatory democracy in the formal education system. Higher levels of funding and logistical support for participatory processes were also recommended, along with improvements to practice through continued innovation and testing of methods, as well as enhanced opportunities for collaborative learning from past experiences. Challenges in representing the values and views of diverse publics were identified as a central concern. The HAP approach provided a systematic way of exploring individual and collective thoughts on a complex topic as well as a means of developing ideas into practical action plans. Reflections on the benefits and shortcomings of this method are offered.
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 | 0.249 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".