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Record W2045194281 · doi:10.1109/istas.2013.6613125

Putting locative technology in its sense of place

2013· article· en· W2045194281 on OpenAlexaff
Glen Farrelly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSense of placeLocative caseMeaning (existential)Computer scienceSociologyAestheticsInternet privacyHuman–computer interactionPsychologySocial scienceLinguistics

Abstract

fetched live from OpenAlex

Our relationship to the places we inhabit and encounter is considered a foundational human experience. As we interact and learn about places, we bestow meaning on such places, forming the mental concept of a sense of place. Although our relationships to place have been considered since antiquity, new ubiquitous technologies, specifically mobile devices and location-based services, may be altering people's everyday relationships to place. This paper reports on an exploratory survey study conducted to provide groundwork for understanding the elements that comprise sense of place and the role location-based services may play. It was found that sense of place arises from diverse information sources, is multimodal, and is individualistic. The survey confirmed the importance of personal experience as a valuable and primary means to form a sense of place. Yet, respondents engaged in a diverse range of information behaviour, which was integral in forming their sense of place. The functionality and information provided by location-based services worked with personal experience and social elements that foster a sense of place.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0060.053
Scholarly communication0.0140.019
Open science0.0010.014
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.314
Teacher spread0.296 · 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 designTheoretical or conceptual
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

Citations5
Published2013
Admission routes1
Has abstractyes

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