Bibliographic record
Abstract
The concept of degrowth or planned economic contraction – through strategies such as work-time reduction, part-time work, job sharing and flexible work, and the simultaneous expansion of social security nets through policies such as universal basic income and universal social protection – has received significant attention within some European countries in recent years. We draw upon domestic, international, and cross-sectoral evidence in this chapter to understand how the concept of degrowth or planned economic contraction might be reconciled with gender equality and social justice to guide public policy and practice agendas in Canada and other industrialised countries. We reviewed the existing scientific and practitioner literature published within the past 14 years (2010–2024) on degrowth, gender equality, and social equity, including within the context of the COVID-19 pandemic (2020–2022), to identify the potential impacts that degrowth in industrialised countries may have on women, gender relations, gender equality, and social equity. Our findings suggest that a democratically planned, yet adaptive, sustainable, equitable and redistributive downscaling of the economy may lead to a future where people in Canada and other affluent countries can live better with less. COVID-19 may serendipitously have provided a convincing rationale for degrowth in industrialised countries.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".