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Record W2114380969 · doi:10.32920/ryerson.14637561.v1

Evaluating the web presence of voluntary sector organizations: an assessment of Canadian web sites

2021· preprint· en· W2114380969 on OpenAlexaffabout
Wendy Cukier, Catherine A. Middleton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBusinessPrivate sectorVoluntary sectorThe InternetUsabilityWeb presenceGovernment (linguistics)TurnoverPublic relationsKnowledge managementMarketingWorld Wide WebPolitical scienceComputer scienceManagementEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.096
GPT teacher head0.420
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2021
Admission routes2
Has abstractyes

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