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Regional economic development authorities in Saskatchewan

2007· article· en· W8970004 on OpenAlexaboutno aff
Kiley Frantik

Bibliographic record

Venue˜The œJournal of emergency medicine · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental planningEconomic growthPolitical scienceRegional scienceEconomics

Abstract

fetched live from OpenAlex

This thesis provides an analysis of Regional Economic Development Authorities (REDAs), which were created by the Saskatchewan government in 1993. It examines the political and economic factors that led to the creation of the REDAs; the design of the organization and funding framework for the REDAs; the main challenges that REDAs face today, and offers recommendations for consideration in the future. The thesis reveals that REDAs were created in response to the challenges associated with the changing global economy. They were based on the principles of Community Economic Development (CED), a grassroots approach that encourages individuals in a community to support and facilitate new and existing businesses in order to contribute to the strength of a community’s economy. This approach was created with the understanding that rather than leading the process, as it had done in the past, the government would facilitate the efforts of these organizations through the provision of resources and support. In an effort to coordinate the CED services being offered in the province, REDAs were established on a relatively large regional basis. Operating as umbrella organizations, REDAs both monitor CED efforts in their respective regions and deliver them. The Saskatchewan government has positioned itself as a partner, offering financial resources that REDAs can rely on and also organizational development and technical support. The analysis provided in this thesis demonstrates that while CED is a popular approach taken by governments at both the federal and provincial level, there must be a considerable degree of commitment to the process to be effective. In the case of the Saskatchewan government, the capacity of these authorities has been enhanced substantially due to the government’s efforts to develop an organizational framework, deliver support through education and training in CED, commit to an evaluation procedure and provide funding on which the authorities can rely. This thesis reveals that the main challenges REDAs face today are largely associated with inadequate financial resources. One of the greatest assets to the organizations is the dedication of qualified staff. With increasing workloads however, REDA staff are reaching their limit in terms of what they can accomplish and without the financial capability to alleviate these pressures by hiring and retaining talented and dedicated staff, the capacity of the CED process becomes significantly restricted. This thesis recommends that to ensure the continuing success of REDAs in the future the following initiatives should be undertaken. First, the provincial government must consider the workload being placed on each REDA. With every new layer of responsibility, there must be an equal level of human and financial support. Second, a review of the funding formula is necessary. With varying levels of capacity, it is not necessary for each REDA to receive equal funding. Finally, if the program is expected to improve, an ongoing evaluation procedure, much like the one in place, will continue to be necessary.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.051
GPT teacher head0.283
Teacher spread0.233 · 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 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".

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Citations0
Published2007
Admission routes1
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