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Record W4398480099 · doi:10.7910/dvn/nhzvcq

Replication Data for: Measuring Accountability in Interlocal Agreements between Indigenous and Local Governments

2023· dataset· en· W4398480099 on OpenAlexaffabout
Tyler Girard, Zachary Spicer, Jen Nelles, Christopher Alcantara

Bibliographic record

VenueHarvard Dataverse · 2023
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsWestern UniversityYork University
Fundersnot available
KeywordsReplication (statistics)AccountabilityIndigenousPolitical scienceBusinessMedicineLawBiologyVirology

Abstract

fetched live from OpenAlex

Interlocal agreements are becoming an increasingly popular policy tool for facilitating intergovernmental coordination and cooperation in Canada and the United States. Surprisingly, Indigenous and local governments are also turning to these agreements to accomplish their goals despite long histories of colonialism, exploitation and dispossession by the settler state towards Indigenous communities. To what extent do interlocal agreements between Indigenous and municipal governments require stringent accountability measures to facilitate intergovernmental coordination? Using a hierarchical Bayesian item response theory model, we explore this question by analyzing 317 interlocal agreements between Indigenous and municipal communities in Canada. We find that accountability strength varies significantly across agreements, contrary to our expectation that accountability requirements would be strong across agreements due to the long history of colonialism. We also find that some of the variation may be a function of the policy area addressed by each agreement, although this finding is likely the result of measurement uncertainty in our estimates.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.012

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 head0.060
GPT teacher head0.277
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes2
Has abstractyes

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