Dialogues of digital commons and equitable resilience
Bibliographic record
Abstract
During the first wave of 2020 pandemic, a rise in numbers and popularity of commons-based initiatives was observed worldwide, either in digital or cyber-physical form. From the open-source distribution and production of healthcare equipment to the installation of community fridges, such initiatives have influenced the resilience potential of communities. This research analyses disasters as an outcome of vulnerability and risk and seeks links between resilience and commons-based initiatives. It places the emergent digital and cyber-physical commons-based initiatives within metropolitan ecosystems and proposes the measurement of their reflections on the resilience of greater areas. That way, an equitable perspective on resilience measurements is proposed through the analysis of bottom-up initiatives and the inclusion of underrepresented groups. The paper consists of a literature review in the fields of resilience, social capital, commons-based initiatives and ecosystems, providing examples from Boston (MA, USA), Medellín (Colombia) and Athens (Greece). This research, being published after the first shock and within the constant stretch of the 2020 pandemic, aims at opening a discussion and adding to the academic knowledge a more equitable resilience perspective, as well as supporting and framing the impact of commons-based initiatives.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".