Bibliographic record
Abstract
Telework is growing as an alternate work arrangement in both public and private sectors. Advocates of this new form of working claim that telework enhances employee efficiency and reduces work-related expenses, such as overhead for businesses and travel expenses for employees. However, alternate work experiments not only redesign the physical workplace, but also restructure work itself. The purpose of this study was to examine the impact of telework on the work done by information professionals. Through combined methods of participant observation, interviews, and diaries, the work of twenty pairs of teleworking and at-office library and information professionals, matched by job, was investigated. The study revealed that although teleworkers and at-office workers performed similar amounts of professional and clerical work, work processes differed between the two groups. Teleworkers were often missing the information and collegial interaction necessary to complete work tasks at home. As a results, teleworkers tried to cope by repeating parts of tasks at home and by dividing work tasks according to available resources. At-office workers often compensated for absent teleworking colleagues by performing additional work tasks or by helping teleworkers locate work-related information. These improvised means of working revealed that teleworkers in this study were not persons working alone with control over all aspects of their work.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".