Managing visibility for career sustainability: a study of remote workers
Bibliographic record
Abstract
Changing patterns of work challenge the notion of employees having a designated workplace predicated on physical presence. These changes have been enabled by developments in technology, where an increasing number of employees can work from remote locations, relying on communication technologies to facilitate their interaction with colleagues, managers and customers. This chapter explores the implications of these developments for career sustainability. Drawing on a large study of remote workers in Canada, we demonstrate how, in addition to meeting formal performance targets, there was a perceived need to maintain and enhance visibility in order to ensure career progression and continued employment. Since remote working may impede visibility, the chapter explores how participants managed relationships with colleagues and clients in order to maintain and/or enhance their visibility and related career opportunities. It also reports how they tacitly accepted, rather than challenged, the impact of visibility on their careers and in so doing demonstrates the continuing importance of face-to-face interaction and physical presence for maintaining professional networks. The implications of these findings are discussed, including the need for organizations to review existing HR policies, particularly those relating to careers, when different forms of working are utilized.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".