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Record W7029575405

Lasītāju klubi Latvijas publiskajās bibliotēkās

2020· dissertation· lv· W7029575405 on OpenAlexaboutno aff

Bibliographic record

VenueE-resource repository of the University of Latvia (University of Latvia) · 2020
Typedissertation
Languagelv
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive dissonanceField (mathematics)Reduction (mathematics)Context (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Bakalaura darbs “Lasītāju klubi Latvijas publiskajās bibliotēkās” izstrādāts ar mērķi noskaidrot Latvijas publisko bibliotēku lasītāju klubu mērķus, dalībnieku piesaisti, literatūras atlasi, kā arī šo klubu dalībnieku tikšanās reižu struktūru. Pētījuma teorētisko bāzi veido Alberta Banduras (Albert Bandura) sociāli kognitīvā teorija (Social Cognitive Theory), Leo Festingera (Leo Festinger) kognitīvās disonanses teorija (Cognitive Dissonance Theory), Čārlza Bergera (Charles Berger) un Ričarda Kalabrīsa (Richard Calabrese) nedrošības mazināšanas teorija (Uncertainity Reduction Theory) un Stenlija Fiša (Stanley Fish) lasītāju reakcijas sociālā teorija (Reader-responce Criticism). Lai sasniegtu izvirzīto mērķi, tika analizēta teorētiskā bāze un iepriekš veiktie pētījumi, kā arī tika veikta Latvijas publisko bibliotēku darba pārskatu analīze, izstrādātas attālinātās intervijas ar 12 Latvijas publisko bibliotēku darbiniecēm, apkopoti un analizēti iegūtie rezultāti, izdarīti secinājumi. Pētījuma rezultāti norāda, ka lasītāju klubu aktivitāte Latvijas publiskajās bibliotēkās nav noteicama pēc kāda parametra, bet salīdzinot dokumentu analīzē iegūtos datus, to darbība divu gadu laikā ir pieaugusi. Lasītāju klubu darbība un struktūra ir atkarībā no to dalībnieku aktivitātes un organizatoru iespējām tikšanās reizes padarīt lasītājiem interesantas. Lai lasītāju klubos tiktu piesaistīti jauni dalībnieki, jāattīstās ne tikai pašam lasītāju klubam, bet arī bibliotēkai kopumā.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0060.002
Research integrity0.0010.001
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 head0.015
GPT teacher head0.193
Teacher spread0.178 · 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 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
Published2020
Admission routes1
Has abstractyes

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