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Record W4415586491 · doi:10.21083/crrf.v30i1.7472

Understanding Canadian Rural Research Centres

2025· article· W4415586491 on OpenAlexaboutno aff
Brennan Lowery, Marc Yvan Valade

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsVitalityLeverage (statistics)SustainabilityRural managementWork (physics)Rural areaRural historyRural sociology

Abstract

fetched live from OpenAlex

The vitality and sustainability of rural communities in Canada requires supports, policies, practices and people, dedicated and creative people. Rural research centres seek to aid in supporting rural vitality by offering needed information through researching on the benefits of investments, innovation, and durability of rural life, as well as sustaining important partnerships with diverse networks of stakeholders in rural regions. This panel will share recent survey results from the RPLC network that is inviting collaboration among Canadian rural research centres, the Rural Research Centres Network (R2CN). Among other findings, the R2CN is inviting more engaged research through community partner collaborations and working creatively to better leverage the resources and insights of rural research centres (RRC). The report writing and data collection and policy advocacy work that emerges from RRC has had great impact in Canada and elsewhere. Might we be in a time for reinvigoration and repositioning of these centres for a more central role in the discourse on the vibrancy of rural life? Come share your ideas on the importance of facts driving policy along with the power of story telling and community building for rural life in Canada.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.292
Teacher spread0.215 · 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
Published2025
Admission routes1
Has abstractyes

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