Archivists and Technological Competencies: A Conversation about Archival Education
Bibliographic record
Abstract
Archivists are working in a rapidly changing environment and it can be challenging to be aware of and proficient with current and emerging technologies. Donors, researchers, and other stakeholders have an expectation that archivists are able to manage and provide access to records in all formats. It is imperative that archivists develop technical competencies in order to engage with records, record systems, and stakeholders. An examination of the current Canadian landscape will allow us to have an effective discussion about technical competencies and identify any strengths, weakness, opportunities, and threats to developing these skills. Building on previous research, this session will present the results of a content analysis of job postings, graduate-level course descriptions, and professional development course descriptions. The objective of this session is to discuss what technical competencies are expected of archivists and the role that they play in archival education and professional practice.
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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 | 0.044 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.049 | 0.052 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".