The participatory futures method: An approach to co-projecting smart urban neighbourhood places in resource-scarce communities
Bibliographic record
Abstract
For smart urban technologies to enhance the current and future urban experiences of residents of cities in Africa, interventions in the urban environments must be considered from an ethical perspective. This is important as urban environments are increasingly becoming the habitat for the majority of people on this planet, and rapidly evolving and increasingly emerging smart urban technologies have the capacity to be immensely socially disruptive. Responding to the question of how CI researchers can employ participatory methods to better understand the preferences of citizens in African cities for the inclusion of smart technologies in their urban environments, this article initially describes the conceptual design of a novel co-design research method, the participatory futures method (PFM), which integrates concepts and techniques originating in the field of experiential futures with the design research method of generative tools. Thereafter, the iterative refinement of the PFM through a series of pilot workshops involving participants from the neighbourhood of Westbury, a resource-scarce urban community in Johannesburg, South Africa is reflected upon. In addition to descriptions of the workshops, the approach taken for analysing and synthesising the data generated in the workshops is outlined and critically reflected upon with particular regard to the capacity of the PFM method to generate meaningful insights pertaining to the Westbury community’s preference for smart places. This research extends the knowledge of community informatics by articulating how the rigour of experiential futures methods for futures-orientated inquiry can be integrated with the reflective qualities of generative tools capable of eliciting latent needs, to orientate participatory encounters with community members that are meaningful to both the discipline and participants. Lastly, this research provides a detailed account of how participatory research practiced in and with under-resourced communities anticipate the potential positive impact of smart technologies in their urban environments. As such, this study contributes a participatory perspective of CI design research, from and by researchers in the global South, a context often marginalised by Western-orientated informatics research.
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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 | 0.089 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".