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Record W4206260756 · doi:10.24917/20833296.172.7

Znaczenie stereotypów w postrzeganiu atrakcyjności Nowej Huty dla odwiedzających i mieszkańców

2021· article· en· W4206260756 on OpenAlexaboutno aff
Michał Żemła, Rafał Woronkowicz

Bibliographic record

VenuePrzedsiębiorczość - Edukacja · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Development and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ResidencePerceptionContext (archaeology)TourismGeographyEmpirical researchPsychologyDemographySociologyArchaeologyMathematicsStatistics

Abstract

fetched live from OpenAlex

The paper undertakes the broadly understood perception of tourism space. The research aim is to present the topic in the context of Nowa Huta – the youngest quarter of Krakow (Poland). Empirical data was gathered on the basis of questionnaires distributed among 400 permanent and temporal residents of Krakow and the closest neighburhood of the city. The main hypothesis is related to the existence of differences in the perception of Nowa Huta according to the place of residence of respondents. On the basis of analysis of the empirical material it was proved that the majority of respondents are familiar with the quarter. In their opinion, Nowa Huta is presented as a dangerous, grey, and scary place. Positive perception of the quarter is typical mainly its residents. The results might be used to determine the directions of the future development of Nowa Huta to improve its image and become more attractive for tourists and residents. The results also prove the relationship between the familiarity of the place and its positive perception.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.306
Teacher spread0.281 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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