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Record W3093969454 · doi:10.1002/pra2.331

Success factors affecting digital literacy training initiatives led by local community organizations

2020· article· en· W3093969454 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2020
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTraining (meteorology)LiteracyPublic relationsData collectionLocal communityMedical educationCommunity organizationInformation literacyPolitical scienceBusinessPsychologySociologyPedagogyMedicineGeography

Abstract

fetched live from OpenAlex

Abstract This paper describes an in‐progress research study investigating the factors affecting the success of digital literacy training initiatives led by local community organizations, including public libraries. Data were collected from two public libraries and five community organizations in two cities in Canada. Data collection comprised interviews with 14 administrators, six training instructors, 20 clients of training programs, the analysis of training documents, observations of six training sessions, and a survey administered to 20 clients. The study involved two rounds of data analysis. The first round is complete. The second round is currently underway and its results, in addition to those from the first round, will be disseminated at the ASIS&T Annual Meeting. Goals of the study are to identify factors affecting the success of digital literacy training opportunities led by local community organizations, and to elicit recommendations for practice for local community organizations to follow.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.018
Open science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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