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Record W1586876235

Recognizing the Commons in Coastal Forests: The Three-Mile Limit in Newfoundland, 1875-1939

2006· article· en· W1586876235 on OpenAlexaffvenueabout
Sean Cadigan

Bibliographic record

VenueNewfoundland and Labrador Studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDiversification (marketing strategy)FishingNatural resourceResource (disambiguation)CommonsBusinessAgriculturePopulationGovernment (linguistics)Quarter (Canadian coin)Natural resource economicsCapital (architecture)Investment (military)EconomyGeographyFisheryEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

DURING THE LAST QUARTER of the nineteenth century, Newfoundland governments were committed to securing industrial diversification through a national economic policy of landward development. Although individual governments varied in the details of their policies, most aimed to open up the interior of the island to agriculture, mining, and forestry development through the construction of a railway system. While governments acknowledged the social and economic importance of the cod and seal fisheries, most believed that these marine industries provided an inadequate economic basis for the colony’s growing population. Persistent downturns in the fishing industry meant that governments grew desperate to attract new industries, and gave support and natural resource rights to industrial interests such as the various Reid railway-related enterprises and the Harmsworth pulp and paper project at Grand Falls. Critics of these deals occasionally suggested that industrial promoters and developers enjoyed what amounted to resource “give-aways,” but the Newfoundland government found it difficult to attract capital investment to resource sectors that were often marginal by international comparison. 1

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.267
Teacher spread0.240 · 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.

Study designObservational
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

Citations3
Published2006
Admission routes3
Has abstractyes

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Same venueNewfoundland and Labrador StudiesSame topicCanadian Identity and HistoryFrench-language works237,207