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Record W4392855912 · doi:10.32920/25412620

Interstitial Learningscapes: Fostering Child-Centric Environments

2024· preprint· en· W4392855912 on OpenAlexaboutno aff
Fahmida Ahmed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationExperiential learningConstruct (python library)LiminalityLearning environmentComponent (thermodynamics)NeutralityFoundation (evidence)SociologySpace (punctuation)PedagogyPsychologyMathematics educationComputer sciencePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

<p>Interstitial: the layered experience between two environments, the in-between moments that are often forgotten. The Architecture of Interstitial Learningscapes aims to construct a layered environment that manifests child-centred experiential learning. Today, except for a few experimental schools, most existing public schools in Canada embody the perfunctory 'factory-school' model predicated on a quantitatively determined programme of indoor and outdoor spaces. There is a need to address the neutrality of these environments and shift towards a more diverse, spatially, materially and sensorially enriched learning environments for children. This thesis investigates the role of liminal spaces in elementary school design to enhance learning and socialization. A foundation of research in pedagogies and architectural case studies supports the design component of this thesis. The opportunities for the 'in-between' experiences will be explored through a series of layered interstitial spaces that unfold between the classroom and enriched outdoor learning environments, enhancing formal and informal learning.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0000.001
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 head0.033
GPT teacher head0.336
Teacher spread0.303 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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