Collective foresight and intelligence for sustainability
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
Non-technical summary Charting robust pathways towards more sustainable futures that ‘leave no one behind’ requires that diverse communities engage in collective foresight and intelligence exercises to better understand global systemic challenges, anticipate the emerging risks and opportunities that disruptions present, and share perspectives on how to respond and inform decision-making. We report on the recent use of an international rapid foresight survey to assess expected societal trends over the next 3 years following the COVID-19 crisis. The results illustrate the power of collective foresight approaches to provide timely, nuanced insights for decision-making across sectors and scales, particularly in times of uncertainty. Technical summary We present the findings of a rapid foresight survey launched in spring 2020 to draw on the collective intelligence of the global community on where the world is headed post-COVID-19. Respondents were asked to (i) assess five key societal trends in the coming 3 years, (ii) provide news headlines they both expect and hope to see, and (iii) assess the role of digital technologies during crises. Analysis of over 2000 responses from more than 90 countries revealed important regional differences in expected societal trends related to sustainability. More respondents in the Global South expected shifts towards less inequality while more respondents in the Global North expected shifts towards a smaller ecological footprint. Qualitative analysis of proposed news headlines revealed four broad themes of focus (environment, equity, health, and economy), and yielded insights into perspectives on critical drivers of change. Finally, the survey report found that the vast majority of respondents were not opposed to digital surveillance in crises. In presenting these results, we explore the value of collective foresight and intelligence exercises in providing pluralistic inputs to decision-making and in complementing more prevalent methods of forecasting. Social media summary Collective foresight exercises with diverse communities can help chart robust pathways to more sustainable futures.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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