GOING VIRTUAL: Some sources of teleworking success and failure
Bibliographic record
Abstract
When a large Canadian company selling high-tech telecommunications equipment propelled two sales departments out of the office, the company (which we will call Telecom) assumed that the knowledge-based work of both groups would readily lend itself to teleworking. But one group floundered, and the other thrived. By looking at the two departments and their different teleworking experiences, we can gain some insight into why some kinds of knowledge work lend themselves to telework and others do not, and perhaps provide some guidance to other organizations that are contemplating telework for some of their own groups of employees. By telework (also known as telecommuting), we mean the work of employees connected to corporate communications networks from their homes or other remote locations. Some teleworkers spend a few days of the week in an office and other days at their home office. In the company we studied, they gave up their company cubicles entirely, working out of their homes or hotelling from a location shared with others. Telecom is a large telecommunications provider spanning two Canadian provinces. Telecoms sales teams do collaborative selling, implementation and aftersale
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".