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Record W2758666039 · doi:10.7202/1041018ar

Les bibliothèques et le développement durable

2017· article· fr· W2758666039 on OpenAlexaffvenueabout
Pascale Guertin, Valérie Poirier-Rouillard

Bibliographic record

VenueDocumentation et bibliothèques · 2017
Typearticle
Languagefr
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsCégep Saint-Jean-sur-RichelieuUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Toits verts, systèmes géothermiques, matériaux durables, recyclage et réutilisation sont des termes qui viennent spontanément à l’esprit lorsqu’il est question de développement durable. Mais que signifie véritablement ce concept ? Comment se manifeste-t-il, concrètement, dans notre environnement, dans nos habitudes de vie ? L’écosystème qu’est la bibliothèque laisse de plus en plus de place aux préceptes du développement durable, par sa nature citoyenne et sa mission de partage du savoir, mais aussi d’inspiration et d’idées nouvelles. Autant au Québec qu’à l’international, divers projets de bibliothèques vertes, axées sur les besoins et le développement de leur communauté, abondent et se démarquent, laissant place à un discours nouveau, où s’entremêlent harmonieusement les considérations et impératifs actuels, sans compromis quant aux besoins des générations futures. Le présent texte reprend des éléments du travail réalisé dans le cadre du cours SCI6372 – Aspects internationaux et comparés de l’information donné à l’été 2016 par Réjean Savard (qui a fait suite au voyage d’études organisé par l’EBSI en Allemagne, à Prague et à Strasbourg à l’été 2016) et propose un survol des initiatives de quelques bibliothèques dans le monde et invite à réfléchir au rôle de la bibliothèque verte.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0110.026
Scholarly communication0.0240.013
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0360.005

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 head0.035
GPT teacher head0.355
Teacher spread0.320 · 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 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

Citations1
Published2017
Admission routes3
Has abstractyes

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