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Record W2824506563 · doi:10.7202/1048925ar

Tools for Rational Development: The Canada Land Inventory and the Canada Geographic Information System in Mid-twentieth century Canada

2018· article· en· W2824506563 on OpenAlexvenueaboutno aff
Shannon Stunden Bower

Bibliographic record

VenueScientia Canadensis Canadian Journal of the History of Science Technology and Medicine · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityState (computer science)Regional scienceKnowledge managementGeographyComputer science

Abstract

fetched live from OpenAlex

From the 1960s through the 1980s, Canadian scientists, resource managers, and computer experts collaborated on two linked undertakings: the Canada Land Inventory (CLI) and the Canada Geographic Information System. CLI was an extensive project that assessed the state of key resources across much of the country, while CGIS was a pioneering effort at computerizing CLI data to support decision-making about resource use. Fundamental components of the Agricultural Rehabilitation and Development Act, CLI and CGIS reflect Canadian innovation in new information-management tools designed to facilitate state goals. This paper examines the production and affordances of CLI and CGIS, and considers the renewed optimism and collaborative relationships that emerged from them. It also examines historical concerns over the limitations of these technologies and explores how CLI and CGIS were oriented to change over space, not time. Ultimately, these technological innovations served to naturalize patterns of inequality and normalize urban-industrial modernity.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.904

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.016
Science and technology studies0.0100.020
Scholarly communication0.0140.006
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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.

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

Citations4
Published2018
Admission routes2
Has abstractyes

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Same venueScientia Canadensis Canadian Journal of the History of Science Technology and MedicineSame topicGeographic Information Systems StudiesFrench-language works237,207