Bibliographic record
Abstract
Western ideas of progress, modernity, economic growth, as well as the commodification of nature have significantly shaped international structures, mindsets, and behaviours, ultimately leading to a range of global sustainability challenges. Yet despite over five decades of sustainable development and corporate social responsibility (CSR) efforts aimed at addressing these issues, the state of the world’s sustainability continues to worsen. This is because the mindset that created the unsustainability is the same one used to address it. That mindset is eurocentrism. Eurocentrism refers to a mindset that regards Western knowledge and values as superior and universal, historically enforced through colonization, slavery, and Christianity, and continues to be reinforced by neocolonialism, globalization, and academia. Consequently, eurocentrism now extends beyond race and geography. By tracing the evolution of eurocentrism from the ‘enlightenment’ to contemporary sustainability efforts, this chapter emphasises the need for more inclusive, pluralistic approaches such as pluriversality, It also champions the inclusion of spirituality, culture, and collectivism in sustainability discourses and practices to advance other ways of knowing, thinking, and doing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".