Decentralization, Autonomy, and Participation in Multi-User/Agent Environments
Bibliographic record
Abstract
This talk will discuss the main features of social computing (Web 2.0) applications and the resulting tendencies in the design of social learning environments. The main features of Web 2.0 applications are the decentralization of resources, repositories, and control; the autonomy of learners, contributors (authors), and learning organizations; and the need for active participation --the necessary “glue” to make it all work. The power of social computing applications depends on the number of users and active contributors. In a large scale open environment, the autonomy of learners and authors/course developers is a given and nobody can force anyone to use a particular system. Ease of use, power, and respect for the user autonomy are important features in attracting learners and developers of educational software or learning materials. The need for autonomy powers a move towards service-oriented or agent-based architectures with clear protocols and languages for interaction between services or agents. Such architectures allow for redundancy and dynamic connection of new functionality or people. The question about ensuring shared meaning is very important. Dictatorial approaches, however, are unlikely to work in an open environment with autonomous participants. To be viable, a standard has to be simple, usable, rich and easily extensible. User modeling focus shifts from centralized servers to a libraries of decentralized processes carried out by individual agents or services in context, for the purpose at hand and expressed as policies that can be edited by users. Along with modeling learner features relevant to the domain of interest (e.g. knowledge, experience, affective features), modeling trust and reputation becomes very important to allow appropriate selection of services, and protect the integrity and privacy of user data. Instructional planning focus shifts to the design of incentive mechanisms to stimulate participation. Various such mechanisms have proved successful in attracting participation of desired type --social comparison, visualizing interpersonal relationships, immediate rewards after desirable actions, and even introducing a virtual currency. I will illustrate all these tendencies with research I have done with my students and colleagues at the University of Saskatchewan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".