Bibliographic record
Abstract
What can migrant ecologies teach us about collective planetary futures? In Planetarity from Below, Emily Yu Zong argues that modern freedom has framed migration in anthropocentric terms, neglecting that migration is also an ecological process. Analyzing a diverse body of migration literature across Australia, North America, and China, she explores how these works unlearn modern capitalist systems of property, individualism, and freedom while imagining collaborative and ecological survival from the margins. Through short stories, memoirs, speculative fiction, poetry, and documentary films, Zong unpacks a decolonial migrant ecopoetics, revealing a pluralist method of worldmaking—from Australia’s oceanic refugee camps, Indigenous Canadian land, and Chinese migrant worker sweatshops, to climate futures. These migrant ecologies imagine freedom “from below” not simply as individual survival or assimilation but as an unruly and contingent process of shared creativity with animals, waters, minerals, waste, and technology. Shifting environmental ethics from individual morality to a political ecology of sustaining life in precarity, Zong introduces decolonial knowledges, imaginations, and praxes that help us expand justice and freedom beyond the human, asking how borderland subjectivities can open new possibilities for multispecies flourishing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".