Doing Archival Appraisal in Canada. Results from a Postal Survey of Practitioners' Experience, Practices, and Opinions
Bibliographic record
Abstract
This paper reports results of a self-administered postal survey of 450 Canadian archivists undertaken between 2003 and 2005. The survey of fifty-eight questions gathered information specifically about appraisal as a work process: how it is done by archivists in Canadian repositories; what resources they use; what problems and issues they have encountered; and in the light of experience, what tools, skills, and knowledge have proven to be important in doing this task. The paper reports the frequencies for eight sections of the survey. It situates the 313 responses (response rate of 70%) within their overall experience, institutional affiliation, and basic demography. The paper also discusses respondents’ opinions on the knowledge, education, and training needed to do appraisal, and assesses the sources for information they use and find useful. After reporting on the respondents’ approach to the task and the methods they use, the paper looks at the problems encountered in doing appraisal and discusses the ideas that archivists have about their accountability for decisions. Further analysis of data from the survey is proposed as the companion to the next phase of research on appraisal, interviewing archivists to explore ideas and issues in depth. RÉSUMÉ Ce texte présente les résultats d’un sondage auto-administré acheminé par la poste auprès de 450 archivistes canadiens entre 2003 et 2005. Le sondage qui comportait 58 questions a permis de compiler des données portant spécifiquement sur l’évaluation comme processus de travail : comment les archivistes effectuent l’évaluation dans des dépôts d’archives canadiens; de quelles ressources ils se servent; quels problèmes et questions ils ont rencontrés; et, à la lumière de leurs expériences, quels outils, capacités et connaissances ont été utiles à la réalisation de cette tâche. Le texte rapporte les fréquences pour huit sections du sondage. Il situe les 313 réponses (taux de réponse de 70 %) dans le contexte des expériences générales, des affiliations institutionnelles et des profils démographiques des répondants. Le texte présente aussi leurs opinions en ce qui concerne les connaissances, l’éducation et la formation nécessaires pour mener une évaluation, et il évalue les sources d’information dont ils se servent et qu’ils trouvent utiles. Après avoir fait le tour des approches des répondants pour accomplir cette tâche et des méthodes dont ils se servent, le texte examine les problèmes rencontrés en faisant l’évaluation, et il explore les idées que les archivistes ont au sujet de leur obligation de rendre des comptes pour leurs décisions. L’auteure propose une analyse plus poussée des données du sondage pendant la prochaine phase de recherche sur l’évaluation, alors que seront interviewés des archivistes dans le but d’explorer plus à fond leurs idées et leurs préoccupations.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".