Researching Informational Technologies of Trust: From Blockchain to Paradata and Digital Archives
Bibliographic record
Abstract
ABSTRACT Information science research has a long‐time interest in diverse informational means of codifying, communicating and maintaining trust as an antecedent and outcome of information, and a central constituent of information practices. While the landscape of the gatekeepers and guarantors of access to trustworthy information for a long time remained relatively stable, since the turn of the millennium, it has evolved rapidly with several new and repurposed social and digital technologies developed and repurposed for mediating and preserving trust. The purpose of this panel is to engage in exploring from theoretical, practical and empirical perspectives such technologies across the information field. The panelists present research conceptualizing, documenting, developing and describing informational technologies of mediating and preserving trust specifically addressing: 1) what different approaches to mediating and maintaining trust exist and can be identified in information science and technology research and practice; 2) how different information technologies of trust influence the understanding and conceptualisations of trust and trustworthy information; and 3) what novel insights from the current state‐of‐the‐art research can be drawn for helping practitioners to empower and put people first when interacting with informational technologies of trust in different areas of the information field, including knowledge organisation and information behaviour and practices, records management, digital preservation, and and development of information systems and services.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".