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Record W2564770808

Organic Surveillance: Security and Myth in the Rural

2016· dissertation· en· W2564770808 on OpenAlexaboutno aff
Stéphanie Marie-Anne McKnight

Bibliographic record

VenueQSpace (Queen's University Library) · 2016
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyComputer securityData sciencePolitical scienceEnvironmental planningComputer scienceGeographyArtLiterature
DOInot available

Abstract

fetched live from OpenAlex

This project looks at the ways Northeastern Ontario citizens in rural communities regulate their private property through traditional and contemporary surveillance means. Through art and objects, this project allows viewers the opportunity to experience surveillance in rural areas through visual and creative ways that encourage interaction and critique. This project defines organic surveillance by looking at the ways ruralists in Markstay Ontario practice surveillance and deterrence which is influenced by characteristics of land, risks and other determining factors such as psychology, resourcefulness, sustainability, technology and private property. Organic surveillance argues that surveillance and deterrence is prevalent far beyond datamining, GPS tracking and social media. Surveillance and deterrence as methods of survival are found everywhere, even in the farthest, most “wild” and forested areas.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.156
Teacher spread0.153 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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