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Record W155954938

Promoting the Websites of Community-Based Organisations

2010· book-chapter· en· W155954938 on OpenAlexaff
Scott Bingley, Stephen Burgess, Gordon Hunter

Bibliographic record

VenueVictoria University Research Repository (Victoria University) · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSophisticationPromotion (chess)Theme (computing)Online and offlineSocial mediaPublic relationsBusinessOnline communityMarketingPolitical scienceSociologyWorld Wide WebComputer scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

Community-based organisations (CBOs) are a widely diverse group of organisations that exist to benefit their membership or promote a wider cause. CBOs are increasingly using websites to assist in carrying out their functions. This paper examines the practices of 35 CBOs from Australia, New Zealand and the UK from the viewpoint of how they use offline and online strategies for website promotion. CBOs employed a mix of offline and online promotion strategies – which appeared to relate to the operations of different types of CBOs. There was a level of sophistication that was not expected by the authors. Another interesting result that emerged from the study was that there was a degree of uncertainty as to how social networking websites fit into the web presence of CBOs. --Australian and New Zealand Marketing Academy (ANZMAC) Conference 2010 held, Canterbury, New Zealand, 29 November - 1 December 2010. Theme: ‘Doing More with Less'

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.053
GPT teacher head0.292
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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