Fronteras Imaginarias: Theorizing Fronterizidad in the Simulated Illegal Border Crossings of El Alberto, Mexico
Bibliographic record
Abstract
Oye, Gloria, aguantaras hasta California?" ("Hey, Gloria, can you make it to California?").I was expecting the significance of this tourist's question to be diffused by nervous laughter, as we crouched, huddled along a dusty path in amongst brush in the dead of night with little moonlight and only our coyote to guide us.The question, instead, was met with awkward silence as we tried to catch our breath.Two hours into this night trek through the desert canyon of El Alberto, Mexico in the heart of the Valle del Mezquital-with our field of vision limited to the person next to us, but imaginatively reaching all the way to the border some seven hundred miles north of the site of this fictional border crossing-the prospect of "making it" "for real" is, in fact, unimaginable.This question, "Can you make it to California?" uttered in the context of this simulated border crossing was an invitation-or perhaps a reminder-for all of us tourists huddled there to immerse ourselves "in the role" of illegal migrant and to try to amplify in our imaginative vistas-by hours, by degrees of hunger, thirst and fatigue, by levels of fear, injury, and threat-this mere fragment of an experience into the kind of journey that it might be, or could be, "for real."In the time that has passed since this caminata nocturna, or "night walk" as the simulation is called, the question, "Can you make it to California?" remains there in that silence both awkwardly and ominously, a hollow echo of a question undoubtedly uttered in desperation by some of the roughly 600,000 plus migrants who attempt to cross illegally into the United States every year. 2 If we think of the border as an interpellating force that hails and constitutes the identities to whom it grants passage or expels, a regulatory force that has come to constitute transnational migrant identities and fronterizas/os in movement across national boundaries, then what constitutive acts take place in a reenactment of this passage?How does a reenacted border crossing lay bare the performative force of the border?Furthermore, in this laying bare what questions does it compel us to ask about the construction of cultural identities made in the image of what Marcial Gonzalez refers to as "the most repressive and racist symbol of demarcation and exclusion produced by capitalism, nationalism, and imperialism-namely, the Natalie Alvarez is an assistant professor at Brock
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".