Border Insecurity: Reading Transnational Environments in Jim Lynch’s Border Songs
Bibliographic record
Abstract
This article applies an eco-critical approach to contemporary American fiction about the Canada-US border, examining Jim Lynch’s portrayal of the British Columbia-Washington borderlands in his 2009 novel Border Songs. It argues that studying transnational environmental actors in border texts—in this case, marijuana, human migrants, and migratory birds—helps illuminate the contingency of political boundaries, problems of scale, and discourses of risk and security in cross-border regions after 9/11. Further, it suggests that widening the analysis of trans-border activity to include environmental phenomena productively troubles concepts of nature and regional belonging in an era of climate change and economic globalization. Cet article propose une lecture écocritique de la fiction étatsunienne contemporaine portant sur la frontière entre le Canada et les États-Unis, en étudiant le portrait donné par Jim Lynch de la région frontalière entre la Colombie-Britannique et Washington dans son roman Border Songs, paru en 2009. L’article soutient que l’étude, dans les textes sur la frontière, des acteurs environnementaux transnationaux – dans ce cas-ci, la marijuana, les migrants humains et les oiseaux migratoires – jette un jour nouveau sur la contingence des limites territoriales politiques, des problèmes d’échelle et des discours sur le risque et la sécurité des régions transfrontalières après les évènements du 11 septembre 2001. Il suggère également qu’en élargissant l’analyse de l’activité transfrontalière pour y inclure les phénomènes environnementaux, on brouille de façon productive les concepts de nature et d’appartenance régionale d’une époque marquée par les changements climatiques et la mondialisation de l’économie.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".