Bibliographic record
Abstract
Before we can exploit new technologies to realize new ways of working, we must be able to imagine innovative possibilities for learning. Organizations seeking to improve the way they work and build knowledge reach for new learning paradigms. Possibilities emerge when exploring learning and working in virtual spaces from social learning perspectives, such as situated learning.. In this chapter, findings from a qualitative case study in a geographically dispersed organization are used as a springboard for exploring the challenges of introducing innovative e-learning initiatives. This chapter adds to our understanding of learning and working in virtual spaces by delving into: (1) workplace practices related to virtual learning and work that facilitate and frustrate new ways of learning; and (2) notions of online community, informal learning, and blended learning which offer promise for re-conceptualizing learning within virtual work spaces. Recommendations are provided to guide the creation of fresh teaching and learning practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.051 |
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".