Bibliographic record
Abstract
In the first months of 2020, the call for papers for the 2022 Special Issue of the Annals of the American Association of Geographers was circulated. It invited papers that engage with multiple forms and meanings of displacements and their geographies: patterns of shifting, dislocation, or putting out of place; substitutions of one idea for another or the unconscious transfer of intense feelings or emotions; activities occurring outside their normal context; and replacements of one thing by another. The COVID-19 pandemic, declared by the World Health Organization shortly after, produced new displacements and intensified existing patterns of displacement and dispossession, including human and more-than-human mobilities and immobilities. At the same time, socionatural displacements—floods, fires, droughts, hurricanes, sea-level rise, species loss, and dislocation—were the backdrop to the displaced and deferred hopes of the 2021 United Nations Climate Change Conference. The twenty-seven articles in this special issue contend with how we as geographers conceptualize and theorize displacements; the range of sites, spaces, processes, affects, scales, and actors we study with to understand them; and what is at stake politically in how we research displacements. It is also a pandemic archive of academic labor, in which we find traces of displacements within and beyond our discipline.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".