InterPARES Trust AI - RA05 - Users' approaches and behaviors in accessing records and archives in the perspective of AI: a global user study Phase 1 – Italian study
Bibliographic record
Abstract
This study was organized within the International research project InterPARES Trust AI, whose P.I. is prof. Luciana Duranti, luciana.duranti@ubc.ca , with the Co-Direction of prof. Muhammad Abdul-Mageed, muhammad.mageed@ubc.ca, both at the University of British Columbia, Vancouver, Canada. The study RA05 - <em>Users' approaches and behaviors in accessing records and archives in the perspective of AI: a global user study</em>, is coordinated by Pierluigi Feliciati, University of Macerata, Italy, and carried out by a team composed of: Jessica Bushey, San José State University, California, USA, Giorgia Di Marcantonio, University of Macerata, Italy, Darra Hoffmann, San José State University, California, USA, Lorette Jacobs, University of South Africa, Tshepho Mosweu, University of Botswana, Adele Torrance, Ingenium, Canada. the field research aims to produce more evidence about how the functions within reference and access in adopting AI support could be articulated and match those metrics with data from real users. Data is lacking internationally on the actual User Experience of accessing records and archives and no shared methodologies have been accepted worldwide. Without shared protocols and metrics, users' behavior and satisfaction (quality of access) studies are typically undertaken within specific services in their local context. How much do we know about how digital archival users perform their research? Do they use personal names? Places? Dates? Functions? Subjects? Are they comfortable with the language of interfaces and records? Are they willing to share their research data to improve archival services by adopting AI tools? The data collected by involving a sample of final users could give an idea about their satisfaction with existing digital archival reference and access services and their actual awareness, expectations and concerns about adopting AI tools to reference and access archival records. The study, started in the first weeks of 2022, was integrated with the collection of an updated bibliography to document the state-of-the-art about digital archival users' studies. The Italian online survey was launched on October 10<sup>th</sup>, 2022 in collaboration with four Italian State Archives: Turin, Milan, Venetia and Ascoli Piceno, and closed on December 11<sup>th</sup>. Together with the survey, to support Archives in engaging their selected users, we prepared a poster to be printed and posted in the reading rooms and a template letter to be sent to users’ mailing lists, if existing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".