Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".