On-line Learning For Abused Women and Service Providers In Shelters: Issues Of Representation And Design
Bibliographic record
Abstract
The challenge and potential of Internet technology to deliver learning services to increasing numbers of diverse learners who may not be included in formal continuing education settings are beginning to be addressed. VIOLET (http://www.VIOLETnet.org), a web site for abused women and their service providers, is designed to provide relevant legal information, an on-line community for support and sharing of experience and information, and on-going updates of legal information and community services. The project emerged out of a unique collaboration among women in many local and national communities and organizations and the Legal Studies Program at the Faculty of Extension, University of Alberta. Together they developed a safe space on the Internet for abused women, inclusive of gender, language, cultural, and learning style issues.A technology that widens access to information may also present barriers to access. In this paper we explore some of these barriers and their implications for the design of information resources and learning environments for abused women. These issues are described using an account of the formative evaluation of the project's web site.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".