Evaluating the web presence of voluntary sector organizations: an assessment of Canadian web sites
Bibliographic record
Abstract
In recent years, considerable attention in Canada has been focused on strengthening relationships between the voluntary sector, government and industry. Information technology is an important tool for the sector, but systems are often difficult and costly to purchase and maintain—particularly for small- and medium- sized groups. Unlike e-business and the private sector, little attention has been paid to how the Internet can be used in the voluntary sector. This article addresses three specific research questions: 1) How are national Canadian voluntary sector organizations using Web sites? 2) How well-designed are these Web sites in terms of usability and aesthetics and 3) How can Canadian voluntary organizations improve their Web sites to meet organizational objectives? Some 184 English language, national Canadian voluntary organizations' Web sites were rated, using a standardized tool to assess organizational objectives and to evaluate functionality, navigation and aesthetics. These sites currently offer limited functionality, and many are not well-designed. The article draws lessons from information technology theory and practice to demonstrate how the functionality and design of voluntary sector Web sites (in Canada and elsewhere) can be improved, to better support organizational objectives and to reduce the “digital divide” between the profit and nonprofit sectors.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".