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Record W2474606127 · doi:10.15133/j.ijccr.2007.002

Local Currency Loans and Grants: Comparative Case Studies of Ithaca HOURS and Calgary Dollars

2007· article· en· W2474606127 on OpenAlexaboutno aff
Jeff Mascornick

Bibliographic record

VenueThe Mathematics Enthusiast · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCurrencyBusinessEconomicsPolitical scienceMonetary economics

Abstract

fetched live from OpenAlex

This study examines the rationale(s) that recipients have for participating in HOURS-based local currency loan and grant programs. Case studies, based on interviews of both loan and grant recipients and system coordinators, of Ithaca HOURS and Calgary Dollars local currency systems (LCSs) are presented here. Biggart and Delbridge’s (2004) Systems of Exchange typology, which allows for both instrumental (“means calculated”) and substantive (“ends calculated”) bases of rational economic action, provides the theoretical framework for this study. Insight into the rationales that individuals have for seeking a loan or grant can aid HOURS-based LCS coordinators in the development and promotion of these programs. This study also introduces local currency loans and grants to the social science community while demonstrating the applicability of Biggart and Delbridge’s (2004) typology to an understanding of LCSs and similar economic exchange networks characterized by both instrumental and substantive rationales.

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)0.004
metaresearch head score (Gemma)0.011
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: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0120.008
Scholarly communication0.0040.003
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.097
GPT teacher head0.389
Teacher spread0.292 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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