Equity and Justice should underpin the discourse on Tipping Points
Bibliographic record
Abstract
Abstract. Radical and quick transformations towards sustainability have winners and losers, with equity and justice embedded to a greater or a lesser extent. According to research, only the wealthiest 1–4 % of the global population will radically need to change their consumption, behaviours, societal values and beliefs in order to make space for an equitable and sustainable future for nature and people. However, narratives around many ‘positive’ tipping points, such as the energy transition, do not take into account the entire spectrum of impacts the proposed alternatives could have or still rely on narratives that maintain current unsustainable behaviours and marginalise many people. One such example is the move from petrol-based to electric vehicles. An energy transition that remains based on natural resource inputs from the Global South must be unpacked with an equity and justice lens to understand the “true cost” of this transition. Another is the role of ‘nature-based solutions’ to address climate resilience, where ‘nature’ in some parts of the world needs to be maintained as an offset for the continued lifestyles of the wealthy, usually in different parts of the world from where this nature is supposed to be maintained. There are two arguments why a critical engagement with these and other similar proposals needs to be made. First, the idea of transitioning through a substitution (e.g., of fuel), whilst maintaining the system structure (e.g., of private vehicles) may not necessarily be conceived as the kind of radical transformation being called for by global scientific or governmental bodies like the IPCC and IPBES. Secondly, and probably more importantly, the question of positive for whom, and positive where must be considered. In this paper, we unpack these narratives in the context of what they mean for the idea of positive tipping points using a critical decolonial view from the South.
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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.114 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".