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Record W206458594

PRESIDENT'S ANNUAL REPORT MESSAGE

2007· article· en· W206458594 on OpenAlexaboutno aff
Mario J. Calla, Bruno M. Suppa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsThrivingPublic relationsCommunity developmentWork (physics)Investment (military)Service (business)Resource (disambiguation)BusinessCapacity buildingPolitical scienceEconomic growthSociologyMarketingEngineeringComputer sciencePoliticsEconomics
DOInot available

Abstract

fetched live from OpenAlex

It takes a village…This phrase comes to mind when I reflect on the past year. In 2007, what COSTI defines as “community development” represented the organization’s focus and greatest area of growth. Services that cater to the individual alone, and to his or her transition to Canadian life are not enough. Equally important is the need to build welcoming communities. To build successful, thriving communities, systemic barriers must be broken down and a network of community supports created; leadership and capacity building must also be developed. These are critical aspects to achieving a harmonious, inclusive and equitable multicultural society. COSTI makes a significant investment in community development and takes a broad-based approach that is intrinsic in all service areas. COSTI staff contributed over 12,000 hours and participated in over 40 sectorspecific, ethno-specific and issue-specific work groups and coalitions – whether to develop or coordinate services among providers, support a local community initiative, conduct research, or raise awareness and recommend solutions to policymakers. COSTI also supports sector capacity building through projects that extend resources and support to various community agencies through resource development, training and partnerships.

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)0.001
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.588
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.264
Teacher spread0.228 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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