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References

2017· other· en· W4211144643 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismContext (archaeology)PublishingHospitalityLibrary scienceManagementPolitical scienceSociologyGeographyLawEconomicsComputer scienceArchaeology

Abstract

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Citation (2017), "References", Knowledge Transfer to and within Tourism (Bridging Tourism Theory and Practice, Vol. 8), Emerald Publishing Limited, Bingley, pp. 327-375. https://doi.org/10.1108/S2042-144320170000008027 Publisher: Emerald Publishing Limited Copyright © 2017 Emerald Publishing Limited Aarstad, Ness, & Haugland (2015) Aarstad, J. , Ness, H. , & Haugland, S. (2015). Innovation, uncertainty, and inter-firm shortcut ties in a tourism destination context. Tourism Management, 48, 354–361. Achaerandio, Bigliani, Curto, & Gallotti (2012) Achaerandio, R. , Bigliani, R. , Curto, J. , & Gallotti, G. (2012). Smart cities analysis in Spain 2012 – The smart journey. IDC, Madrid. Retrieved from http://www.portalidc.com/resources/white_papers/IDC_Smart_City_Analysis_Spain_EN.pdf Adam, Cobos, & Liu (2007) Adam, J. , Cobos, X. , & Liu, S. (2007). Travel 2.0: Trends in industry awareness and adoption. New York, NY: New York University and PhoCusWright Inc. 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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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.378
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.013
Science and technology studies0.0030.001
Scholarly communication0.0120.009
Open science0.0030.005
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.6220.596

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 head0.086
GPT teacher head0.431
Teacher spread0.345 · 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 designNot applicable
Domainnot available
GenreOther

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".

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