Digital literacy, the use of collaborative technologies, and perceived social proximity in a hybrid work environment: Technology as a social binder
Bibliographic record
Abstract
The post-pandemic era is paving the way for the sustainable implementation of a new way of working that combines remote and in-person work, called hybrid work, which is enabled by Information and Communication Technologies, or ICTs. The use of these technologies in a hybrid work environment increases the importance of digital literacy in maintaining communication and social interactions. This paper aims to examine the role of digital literacy (and particularly the technical, cognitive, and socio-emotional dimensions) in the use of collaborative technologies, as well as the effect of the use of these technologies on perceived social proximity in a hybrid work environment. Data were collected from 5141 public service workers in 2023. The hypotheses were tested using structural equation modelling. The technical dimension of digital literacy is associated with the use of collaborative technologies, though the cognitive and socio-emotional dimensions are not. The use of collaborative technologies is associated with perceived social proximity. The study's findings highlight the importance of technical digital literacy, as well as the positive effect of the use of collaborative technologies on perceived social proximity in the hybrid work environment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".