Bibliographic record
Abstract
Introduction This chapter explores the notion of ‘citizen engagement’ from a socio-cultural and user-centred perspective. It does so by drawing on a set of InterPARES Trust (hereinafter ITrust) studies which looked at the relationship between records (understood both in a specific sense and as information objects), record systems (including any technologies used to manage all kinds of data and information) and the users of such records and systems (e.g. creators, subjects, administrators, curators, end-users) with an emphasis on the kind of human engagement, or participation, that arises from, and gives shape to, such a relationship. The contributions gathered in this chapter try to answer questions such as: • How do people perceive born-digital objects? What makes them trust the records and the institutions in charge of them? (Questions addressed in EU27 User Perceptions of Born-Digital Authenticity (Bunn et al., 2016)); • How has citizens’ communication with the government changed over time? Has the nature of patents and petitions – two very common legal genres enabling the interaction between people and institutions – shifted in the digital age? (Questions addressed in NA13 Patents, Petitions, and Trust (Hohmann, 2016; Foscarini, 2019); • How are open government initiatives and civic technologies changing society and the way citizens participate in public matters? Questions addressed by NA08 The Implications of Open Government, Open Data, and Big Data on the Management of Digital Records in an Online Environment (Suderman and Timms, 2016); and EU05 Models for Monitoring and Auditing of Compliance in the Flow from Registration to Archive in e-Register (Strahonja, 2018); • Can social media be used by local governments to increase citizen trust? What can we learn about the administration of social media that results in an increase in trust in government? (Questions addressed in NA05 Social Media and Trust in Government (Franks, 2016)); • Are the users taken into account when appraising an institutional website? What can we learn about citizen engagement through measuring visitor interactions with such websites? (Questions addressed in EU25 Using Web Analytics in Appraisal of Records on the Foreign Ministry of Israel Website (Schenkolewski-Kroll and Tractinsky, 2016)).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".