Do responses to the COVID-19 pandemic anticipate a long-lasting shift towards peer-to-peer production or degrowth?
Bibliographic record
Abstract
The COVID-19 pandemic simultaneously triggered a sudden, substantial increase in demand for items such as personal protection equipment and hospital ventilators whilst also disrupting the means of mass-production and international transport in established supply chains. Furthermore, under stay-at-home orders and with bricks-and-mortar retailers closed, consumers were also forced to adapt. Thus the pandemic offers a unique opportunity to study shifts in behaviour during disruption to industrialised manufacturing and economic contraction, in order to understand the role peer-to-peer production may play in a transition to long-term sustainability of production and consumption, or degrowth. Here, we analyse publicly-available datasets on internet search traffic and corporation financial returns to track the shifts in public interest and consumer behaviour over 2019 - 2020. We find a jump in interest in home-making and small-scale production at the beginning of the pandemic, as well as a substantial and sustained shift in consumer preference for peer-to-peer e-commerce platforms relative to more-established online vendors. In particular we present two case studies - the home-made facemasks supplied through Etsy, and the decentralised efforts of the 3D printer community - to assess the effectiveness of their responses to the pandemic. These patterns of behaviour are related to new modes of production in line with ecological economics and as such add capacity to a broader prefiguration of degrowth. We suggest an adoption of a new "fourth wave" of DIY culture defined by enhanced resilience and degrowth to continue to add capacity to a prefigurative politic of degrowth.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".