Organizing personal digital information: an analysis of faculty member activities
Bibliographic record
Abstract
Purpose The objective of this paper is to document and analyze the organizational activities of faculty members using a personal information management (PIM) framework developed by Jacques (2016). Design/methodology/approach Interviews were carried out with seven faculty members, focusing on their personal information organization practices as they relate to their academic activities. These interviews took the form of a guided tour of informants' digital workspaces. Findings Analyses focused on PIM activities make it possible to identify the different strategies adopted by faculty members to organize their academic personal information. This qualitative approach highlights four activities involved in the organization of personal information: inclusion, exclusion, apprehension and implementation. It also reveals differences in the ability of faculty members to analyze their own practices. Finally, the relationship to time and memory of PIM practices is examined through the lens of the concepts of virtualization and actualization. Originality/value This research provides a more nuanced understanding of PIM practices, specifically of organizational activities, by considering the meaning of these practices for individuals as part of their daily lives. It aims to foster literacy by facilitating the interactions of individuals with their personal information through educational activities.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".