Roles and Relationships in Virtual Environments: A Model for Adult Distance Educators Extrapolated from Leadership in Experiences in Virtual Organizations
Bibliographic record
Abstract
In this paper, Larkin and Gould’s (1999) activity theory methodology for defining work-related roles, and Burns’ (1963) analysis of organismic organizational form are merged into a model that describes associate and leadership roles and relationships in virtual organizations. The effects of a lack of a shared physical space and face-to-face social interaction, a continual need for learning and collaboration, and the temporalities that characterize roles and relationships in virtual organizations will be explored. This exploration will focus on the challenges that virtual workspace effects have created for two current leaders of virtual organizations. The acquisition and use of leadership power to meet these challenges will be discussed. Gifford and Enyedy’s (1999) activity theory, as well as interview data from six educators, will be used as a basis for drawing parallels between associate and leadership roles and relationships in virtual organizations and learner and instructor roles and relationships in virtual learning communities. These parallels will be extrapolated into a tentative model for pedagogical leadership in virtual learning environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".