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Fluctuations in land values in a rural municipality in southern Québec, Canada

2006· article· en· W1491870145 on OpenAlexaffvenueabout
ÉRIK PROVOST, André Ménard, Ali Frihida, Gérald Domon, Danielle J. Marceau

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversity of CalgaryUniversité de Montréal
Fundersnot available
KeywordsGeographyPeriod (music)Land usePhysical geographySpace (punctuation)Environmental resource managementEnvironmental scienceEcologyComputer science

Abstract

fetched live from OpenAlex

The agro‐forested region of the Haut‐St‐Laurent, in southwestern Québec, in Canada, has served as a laboratory for several years to a multi‐disciplinary research team seeking to understand the interplay of stakeholders and processes influencing the rural space of southern Québec. Following directly in the footsteps of previous research, this study was undertaken to analyze the hitherto neglected but important aspect of fluctuations in land values with respect to geomorphology and land use, during the 1958–1997 period. A geo‐referenced database was built within an object‐oriented geographic information system (GIS) that includes data from the sale of parcels of land within the study area, land registry maps, land use maps, and a geomorphological map. These data were analyzed and sorted through queries addressed to the database. Finally, a statistical analysis was performed to analyze the relationship between sale price, geomorphology and indirectly land use, for the entire study period and for each of its decades. The results show that land value has increased at different times during the past, according to its geomorphological type and land use. These relationships are explained by the important transformation phases that have affected southern Québec during the second half of the twentieth century .

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 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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