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Record W7001106896

Infrastructures in Practice, Market Dynamics, and Historical Railways Tourism: The Appleton’s Guide to the United States and Canada, 1879

2019· article· en· W7001106896 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (University of Udine) · 2019
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsMateriality (auditing)TourismWork (physics)Order (exchange)Dimension (graph theory)Grounded theory
DOInot available

Abstract

fetched live from OpenAlex

This work historically analyses the infrastructural dynamics of the North American railway system and proposes a connection between the concepts of space, materiality and institutional dynamics which can be used for tourism management studies. The theory-building case study (in a grounded theory approach) is based on a BBC travel documentary on the Appleton’s General Guide of 1879. Starting from the concepts of “production of space” and “sociospatial relations”, the introduction of the specific material infrastructure dimension allows us: i) to pinpoint a theoretical framework over four levels (territory, place, scale, networks) in order to study the “institutional dynamics of markets”; (ii) and suggests a possible practice-based turn in destination marketing studies.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.061
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.015
Science and technology studies0.0060.013
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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 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
Published2019
Admission routes1
Has abstractyes

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