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Record W7097385597

The Look of Surveillance Returns Reflection essay: Between demythologizing and deconstructing the map: Shawnadithit’s New-Found-Land and the alienation of Canada.

2013· article· en· W7097385597 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)Reflection (computer programming)Set (abstract data type)AlienationCritical reflectionObject (grammar)Geographer
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.352
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.028
Scholarly communication0.0090.004
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.254
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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