Un/Settlement: Counter-Memories of Migration and Railways in Southern Mexico
Bibliographic record
Abstract
The Ferrocarril Panamericano (Panamerican Railway) has long served as a space of livelihood, exchange and integration for the communities of the Pacific Coast of southern Mexico.Nevertheless, in recent decades, the historic vitality of this railway, its passengers and communities has been buried under narratives of migrant criminality, terror, and victimhood.As a means of lightening the contemporary weight of security imaginations, this dissertation draws on archival and interview data from diverse locations in Mexico to offer a series of countermemories of migration on the Ferrocarril Panamericano in southern Mexico.Each countermemory aims to foreground memories that have been buried and to reconnect historical knowledges that have been separated from one another.It reconnects studies of migration and governance with histories of settlement, transport and state-making.It also highlights the expulsions and dispossessions of today in connection with the transit migration phenomena. SinopsisDesde su inauguración, el Ferrocarril Panamericano ha servido como espacio de sustento, intercambio e integración para las comunidades de la costa del Pacifico al sur de México.Sin embargo, en años recientes, la vitalidad histórica de este ferrocarril y de sus pasajeros se ha enterrado bajo las narrativas e imágenes de criminalidad, terror y victimización de migrantes.Como manera de aligerar el peso de este imaginario securitizado, esta tesis ofrece una serie de contra-memorias de los ferrocarriles y las migraciones en el sur de México.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".