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Record W2621352231 · doi:10.21427/d76x6n

Community Engagement in a Conflict Environment: Reflections on the work of the International Fund for Ireland 1986-2011

2014· article· en· W2621352231 on OpenAlexaboutno aff
Paddy Harte

Bibliographic record

VenueARROW@Dublin Institute of Technology (Dublin Institute of Technology) · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAppreciative Inquiry and Organizational Change
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Work engagementPublic relationsPolitical sciencePsychologySociologyEngineering

Abstract

fetched live from OpenAlex

The International Fund for Ireland, which was set up by the British and Irish Governments in 1986 under the Anglo-Irish Agreement of 1985, was funded by the United States of America, the European Union, Canada, Australia and New Zealand. The International Fund enjoys the support of 31 countries, which is truly remarkable. It is one of the most successful examples of the Irish Diaspora at work in a very tangible way; a point ably captured in the Fund’s 2002 Annual Report where Hon Russell Marshall from New Zealand notes “As a member of the Irish Diaspora, New Zealand was delighted to be invited to join the Fund, and to lend its weight to the search for a permanent peace between the communities of the North, which had given so much to New Zealand’s early history”. The Fund had come into existence as part of an Agreement which did not have whole-hearted support in either part of the island at that time. It also came in the wake of many false dawns. While this had the effect of making life difficult for the fledgling organisation it would, in my view, come to be one of the drivers of its success as it became clear that the International Fund for Ireland (IFI) was part of a much larger story and the beginning of something really significant for this island.

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)0.062
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.005
Science and technology studies0.0680.073
Scholarly communication0.0440.024
Open science0.0070.051
Research integrity0.0220.042
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.111
GPT teacher head0.285
Teacher spread0.174 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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