Meeting technologies and recordkeeping: a preliminary study
Bibliographic record
Abstract
Purpose The purpose of this study is to raise awareness of the benefits and drawbacks involved in using digital technologies for business meetings, and identify key concerns. The shift from in-person to virtual meetings has multiple consequences, some of which impact recordkeeping. Design/methodology/approach Drawing on research from records management, anthropology, organizational theory and computer science, this study establishes the norms of physical meeting spaces and recordkeeping and explores how these norms are challenged as meetings become virtual. Findings Virtual meetings allow for collaboration to work across time and space and offer multiple affordances that do not exist in on-site meetings; however, they also involve the additional barrier of technical access and reduction in user attention. Virtual meetings also enable the creation, capture and sharing of increased contextual data, and this increased documentation challenges traditional recordkeeping models. Meeting technologies are also worryingly invasive. This study shows that concerns over privacy have been dismissed in the design of virtual meeting spaces, and therefore the authors recommend their more thorough consideration. Originality/value Meetings are a pervasive feature of organizational life whose significance has been overlooked in the recordkeeping literature. By bringing together research about in-person and virtual meetings in a novel and necessary way, the authors started to fill a gap and hope to inspire further studies.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".