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Record W2989878845 · doi:10.22215/etd/2019-13832

Going North: A Reflection on Lines at the First Canadian Road to the Arctic Coast

2019· dissertation· en· W2989878845 on OpenAlexaboutno aff
Stephanie Murray

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArtifact (error)Reflection (computer programming)NotationSightGeographySpace (punctuation)The arcticLine (geometry)ArcticSociologyPsychologyComputer scienceGeologyLinguistics

Abstract

fetched live from OpenAlex

The experience of the first Canadian Road to the Arctic Coas informed the trajectory of this thesis which investigates "the line" as a tool that explores and describes spatial understandings: whether as a physical artifact in space (the road), in making (maps, notations, models and drawings), in storylines (with their material consequences) or physical perspectives (as lines of sight); all of which inform thinking and acting in and toward the site of study. A line doesn't necessarily manifest as "the dot that went for a walk", but often as an inclination, a thought pattern, a habit of spatial engagement, an assumption, or a physical act. The lines we draw and imagine, order our spatial and social practices and write the stories of our understandings. Through a series of reflective exercises this thesis looks for ways in which we might begin destabilizing our patterns of seeing, thinking and making.

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)0.004
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0490.027
Scholarly communication0.0100.005
Open science0.0030.006
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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
Published2019
Admission routes1
Has abstractyes

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