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Record W3174627686 · doi:10.18438/eblip29873

Digital Literacy Skills for Family History Research

2021· article· en· W3174627686 on OpenAlexvenueno aff
Jaci Wilkinson, Natalie Bond

Bibliographic record

VenueEvidence Based Library and Information Practice · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsArchivistOutreachInformation literacySet (abstract data type)Family literacyClass (philosophy)LiteracyPoint (geometry)Digital literacyPedagogyMathematics educationMedical educationPsychologyLibrary scienceComputer sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

Objective – In this case study, an archivist and librarian teamed up to teach an introductory course on family history research for adult learners at their university’s lifelong learning centre. In response to students’ relative lack of digital skills, the instructors developed a new set of introductory skills that they believe are essential for genealogy research. Methods – Authors conducted pre- and post-course surveys to determine student expectations and the extent to which the course met those expectations. Authors coded one of these surveys. Results – Course assessment and class activities exposed the need for a set of digital skills that go beyond a literacy framework to assist family history researchers. After analyzing key themes found in pre- and post-course assessment, authors developed a new tool for genealogy instructors titled Introductory Digital Skills and Practices in Genealogy (IDSG). Conclusion – Archivist/librarian collaborations are an excellent way to cultivate needs-based teaching and outreach opportunities in our wider communities, particularly for adult learners. The Introductory Digital Skills and Practices in Genealogy tool is meant to inspire and assist other library professionals who want to teach family history research, serving as a reminder to centre teaching tangible digital skills as a focal point of instruction.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.285
Open science0.0000.000
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 head0.048
GPT teacher head0.274
Teacher spread0.226 · 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
GenreOther

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

Citations4
Published2021
Admission routes1
Has abstractyes

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