Between diving, breathing and splashing: metaphors as lenses to inquire public innovation initiatives
Bibliographic record
Abstract
This paper focuses on metaphors as a methodology to design and reflect on design-led initiatives in the public sector. We are drawing on the experience of a capacity-building program developed in 2020 by Enap (National School of Public Administration) in partnership with teams of the Brazilian federal government, in which we conducted four projects through the metaphor of a collective dive. When analyzing the effects of the projects through conversations with participants, we expanded the metaphor, understanding the reflections as breathing, the project conditions as bubbles and currents, and the results as splashes. We see splashes as variable yet rarely acknowledged outcomes of programs that aim to simultaneously foster public innovation and collective learning. In this paper, we present an example of metaphors acting as boundary objects, adding granularity and nuance to the investigation of public innovation initiatives, and identifying their possible effects in relation to institutional logics and complex structures.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".