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Work of Libraries with the Older Generation: Experience of Germany, Canada and Australia

2021· article· en· W3209755619 on OpenAlexaboutno aff
Ольга Леонидовна Чурашева, E. L. Sharonova

Bibliographic record

VenueBibliosphere · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Behavioral Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Work abroadFace (sociological concept)Population ageingCoronavirus disease 2019 (COVID-19)PopulationQuality (philosophy)Capital (architecture)Economic growthOlder peoplePolitical scienceGeographySociologyGerontologySocial scienceMedicineDemographyEngineeringEconomics

Abstract

fetched live from OpenAlex

The article highlights the experience of libraries working with older generation users in certain foreign countries faced with the modern demographic problem - the aging of the population: Germany, Canada and Australia. The modern forms of work of libraries of these countries with the elderly, which are of interest to Russian libraries, are identified and characterized. Difficulties in the work of foreign libraries with the older generation during the COVID-19 pandemic are separately noted. The authors come to the conclusion, that despite the differences in location, size, departmental affiliation, quantity and quality of the fund, and so on, many libraries in developed countries face the same problem - the increasing number of elderly readers and reconstruct their work in accordance with the new demographic and socio-cultural situation, which requires libraries to develop special services to save the cultural capital of older generations.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.152
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0200.004
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0010.002
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 head0.067
GPT teacher head0.309
Teacher spread0.241 · 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

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