The split ladder of participation: A literature review and dynamic path forward
Bibliographic record
Abstract
Participation of people in decision making and tackling complex problems where there is lack of consensus on the science and relevant values continues to be an important research topic. The 2015 Split Ladder of Participation offered a diagnostic and methodological framework cited in 162 papers. This paper addresses the question: What does a literature review of the Split Ladder of Participation reveal about engaging people in policy problems, effecting transformational change, and how can this diagnostic and strategic tool be improved? A systematic literature review revealed papers that disclose transformational change, or triple loop learning requires: strong social science; social and natural interdisciplinary science; and considerations of uncertainty in environmental science together with uncertainty of values and considerations of power. Policy problems with low levels of trust offer opportunities to engage interest and participation in their resolution. Governments over-utilizing methods limiting participation, may lead to lock-in. Focusing on complex, interconnected problems through participation creates an enduring policy and science, interdisciplinary innovation space. Recognizing participation that is plural, amorphous, and fluid draws attention to power, multiple stakeholder framings of complex issues, advances social learning changing values, norms, power, and the very ethics of science (where social and natural/physical scientists acknowledge and share their power with people), and ultimately advances environmental justice.
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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".