Editing an Electronic Book in Virtual Team: Our Journey
Bibliographic record
Abstract
This paper reveals an in-depth exploration of the issues that arise while working on a multi-editor and multi-author book using electronic media for communication. It discusses every aspect of the process of editing a book from generating ideas to completing the task successfully. The process involves advertising, scheduling, creating an informative social networking site, maintaining quality and giving feedback. Project management skills are required to deal with a range of complex materials that presents challenges in comprehension, structure and consistency. Often, writers are so emotionally involved in their own work that they fill pages with great ideas but fail to express their true intent with the best choice of words. Working with internationally distributed team is challenging as well as motivating. This paper presents detailed anecdote of the process in developing personal connections among editors and with the chapter authors that made the group culture thrive.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".