MétaCan
Menu
Back to cohort
Record W3151233183 · doi:10.29173/iasl7996

The Educational Aspect of School Libraries’ Design and the Students' Territorial Behavior

2021· article· en· W3151233183 on OpenAlexvenueno aff
Snunith Shoham, Zehava Shemer-Shalman

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)IntrusionSittingOrder (exchange)Qualitative researchPsychologyMathematics educationSociologyComputer scienceBusinessSocial science

Abstract

fetched live from OpenAlex

In a study combining qualitative (observation and interviews) and quantitative (questionnaires) methods, the researchers examined the territorial behavior of students and the implications of the internal arrangement of the library space for the students. The territorial behavior observed in the libraries could be seen in where the students decided to sit and how they used library furniture (including chairs, armchairs, computers and the librarian station). Most of the students preferred sitting in a central area in the library. However, for the most part, students chose to sit in places that enabled them to maintain territorial control. While many students sat in groups for both social and study purposes, some of them (particularly the older students) had a need to protect against intrusion into their personal space. This was achieved by sitting at the tables with their backs to the entrance, by body language and, at times, even by using personal belongings to demarcate personal territory. Library planners and librarians must be aware of these “conflicting” needs in order to adapt the design to the behaviors typical of the groups that use the library.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.326
Teacher spread0.288 · 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

Explore more

Same venueIASL Annual Conference ProceedingsSame topicLibrary Science and AdministrationFrench-language works237,207