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Record W2619084659 · doi:10.17613/bxwp3-t7h58

The great public libraries debate - do bricks and mortar still matter? A case study of Canada Water Library, Southwark, London examining current attitudes to a modern library space.

2015· article· en· W2619084659 on OpenAlexaboutno aff
Alison Martino

Bibliographic record

VenueHumanities Commons CORE (Modern Language Association / Columbia University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsMortarPublic spaceCurrent (fluid)Space (punctuation)Architectural engineeringEngineeringLibrary scienceCivil engineeringArchaeologyHistoryComputer science

Abstract

fetched live from OpenAlex

This study aimed to examine in depth a newly-built modern public library to gain insight into attitudes to and perceptions of a modern library space in 2014. It used as a starting point a previous study carried out in 2005 and published by Black (2011) which gathered Mass Observation Archive data on public perceptions of public library buildings at the start of the 21st century. This study aimed to evaluate attitudes a decade on to determine if physical public library spaces still matter. A single case study methodology was used to add detail to the extant general literature on public libraries and provide insight into current attitudes to a purpose-built library in the heart of a community and how it meets users' needs and expectations. A printed questionnaire looking at the library's location, design and use of space was handed out to a sample of users. The results show that the physical library is still important to users who are proud of how it looks and the contribution it makes to their local community and area. The findings also show that the case study library is meeting the needs of its diverse user groups very well by providing access to resources, support, IT and space.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.004
Open science0.0010.000
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 head0.055
GPT teacher head0.236
Teacher spread0.181 · 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.

Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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