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Record W6912846365 · doi:10.5281/zenodo.820609

Evaluation Framework For Cic'S Settlement Programs

2004· article· en· W6912846365 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Service providerLogic modelService (business)Process (computing)StakeholderNegotiationService delivery framework

Abstract

fetched live from OpenAlex

[In 2004] Citizenship and Immigration Canada (CIC) is in the process of evaluating all of its national settlement programs. This report lays out an evaluation framework and logic models for CIC’s programs, and defines some key questions that will guide the program evaluations. The framework aims to clarify the desired results of CIC’s settlement programs for their many stakeholders – CIC staff, Service Provider Organizations (service providers), evaluators, and senior members of the federal government. Like all frameworks, it should be revised and updated as new research expands current knowledge of what interventions lead to successful settlement and integration. Stakeholder groups will use this framework in different ways: CIC staff at National Headquarters will use the framework to define the requirements for evaluations for the settlement programs in 2004. In addition, the output variables and activities contained in the framework may lead to revisions of some iCAMS variables to reflect changes in activities and recommended outputs. CIC staff in the Regions can use the framework to guide contribution negotiations with service providers. For example, service providers who can demonstrate success in key immediate outcomes may be able to make a case for increased funding by meeting CIC objectives for greater service effectiveness. Service Providers can use the framework to help them develop data collection processes that will allow them to demonstrate success in meeting program outcomes. Service providers can also use the program logic models to review their service delivery models so that their activities lead logically to desired immediate outcomes. The first draft of the evaluation framework for CIC’s settlement programs was written in 1999/2000. Since then the framework has been significantly revised, based on dozens of meetings, workshops and conversations with service providers and CIC staff. Interviews with key informants and a review of the literature on settlement and labour market integration also contributed to the revisions. The evaluation framework for LINC was discussed in workshops with service providers in the fall of 2003, and a similar exercise (involving 14 workshops across the country) was carried out for the ISAP and Host programs in February and March 2004. The program logic models in Section 5 distill hundreds of comments and suggestions into four highly simplified diagrams. Following are some key issues that should be kept in mind while reviewing this document. · It is long past time for the social services to evaluate themselves critically, and the settlement sector is no exception. The transition towards accountability and results-based management, while challenging, is essential for the achievement of Canadian policy objectives as well as the individual goals of newcomers themselves. Service providers across the country acknowledge the importance of this process, and generally welcome the opportunity to demonstrate and improve their effectiveness. · On the other hand, data collection is time-consuming for service providers, and should be minimized as much as possible while meeting accountability requirements and evaluation objectives. A common mistake for both funders and service providers is to gather data elements that don’t lead directly to better services or improved impact, but instead reduce efficiency by taking up scarce time. This evaluation framework attempts to minimize data collection to measures that will have a real benefit on improving services to clients and enabling CIC to manage agency performance. · In particular, indicators of intermediate and long term outcomes are expensive to collect and difficult to validate. Over the next few years, starting with the upcoming evaluations, both CIC and service providers should develop and begin collecting meaningful and valid indicators to create a baseline for evaluating settlement programs in the future. CIC should identify service providers and researchers that have demonstrated leadership in this area, and work with them to enhance the capacity of the settlement sector to evaluate itself. Section 5 of this paper – Settlement Program Logic Models – is intended to be used as a stand-alone summary of the evaluation framework. It contains the logic models for all three programs, the logic model for CIC’s settlement programs as a whole, and a few notes on implementation.

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)0.148
metaresearch head score (Gemma)0.078
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: Empirical · Consensus signal: none
Teacher disagreement score0.148
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1480.078
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0100.007
Science and technology studies0.0060.007
Scholarly communication0.0180.008
Open science0.0060.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.290
GPT teacher head0.459
Teacher spread0.169 · 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
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
Published2004
Admission routes1
Has abstractyes

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