The Look of Surveillance Returns Reflection essay: Between demythologizing and deconstructing the map: Shawnadithit’s New-Found-Land and the alienation of Canada.
Bibliographic record
Abstract
Since the time when my article was first published critical cartographic studies have advanced considerably ‘beyond the binaries ’ that it originally sought to challenge (see Del Casino and Hanna, 2006). Here, in this reflection paper, my goal is to build on these advances in inquiry into cartographic representation by exploring how they relate to more recent idealistic and voluntaristic suggestions about moving beyond geographic representation and traditional maps altogether. The rather different possibilities of both ‘non-representational theory ’ and ‘voluntary geographic information ’ are thereby reframed with some re-presentations of my own original argument. To set the scene, though, these reflections begin by revisiting an unsettled binary between roots and routes that was an important inspiration of my article, and which now affords a biographical introduction into its critical geography. In short, I begin by reflecting on how three biographical roots of the article can now be retraced as geo-graphical routes too (hyphens intended, however unsettlingly). 1 The first route was my own movement into and through Canada, studying at the time as a British graduate student at the University of British Columbia in Vancouver and
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".